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Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language processing (NLP) applications such as dialogue systems BIBREF1, question-answering systems BIBREF2, and humor recognition BIBREF3. In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive).Learning affective events is challenging because, as the examples above suggest, the polarity of an event is not necessarily predictable from its constituent words. Combined with the unbounded combinatorial nature of language, the non-compositionality of affective polarity entails the need for large amounts of world knowledge, which can hardly be learned from small annotated data.In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.We trained the models using a Japanese web corpus. Given the minimum amount of supervision, they performed well. In addition, the combination of annotated and unannotated data yielded a gain over a purely supervised baseline when labeled data were small.Learning affective events is closely related to sentiment analysis. Whereas sentiment analysis usually focuses on the polarity of what are described (e.g., movies), we work on how people are typically affected by events. In sentiment analysis, much attention has been paid to compositionality. Word-level polarity BIBREF5, BIBREF6, BIBREF7 and the roles of negation and intensification BIBREF8, BIBREF6, BIBREF9 are among the most important topics. In contrast, we are more interested in recognizing the sentiment polarity of an event that pertains to commonsense knowledge (e.g., getting money and catching cold).Label propagation from seed instances is a common approach to inducing sentiment polarities. While BIBREF5 and BIBREF10 worked on word- and phrase-level polarities, BIBREF0 dealt with event-level polarities. BIBREF5 and BIBREF10 linked instances using co-occurrence information and/or phrase-level coordinations (e.g., “$A$ and $B$” and “$A$ but $B$”). We shift our scope to event pairs that are more complex than phrase pairs, and consequently exploit discourse connectives as event-level counterparts of phrase-level conjunctions.BIBREF0 constructed a network of events using word embedding-derived similarities. Compared with this method, our discourse relation-based linking of events is much simpler and more intuitive.Some previous studies made use of document structure to understand the sentiment. BIBREF11 proposed a sentiment-specific pre-training strategy using unlabeled dialog data (tweet-reply pairs). BIBREF12 proposed a method of building a polarity-tagged corpus (ACP Corpus). They automatically gathered sentences that had positive or negative opinions utilizing HTML layout structures in addition to linguistic patterns. Our method depends only on raw texts and thus has wider applicability.Our goal is to learn the polarity function $p(x)$, which predicts the sentiment polarity score of an event $x$. We approximate $p(x)$ by a neural network with the following form:${\rm Encoder}$ outputs a vector representation of the event $x$. ${\rm Linear}$ is a fully-connected layer and transforms the representation into a scalar. ${\rm tanh}$ is the hyperbolic tangent and transforms the scalar into a score ranging from $-1$ to 1. In Section SECREF21, we consider two specific implementations of ${\rm Encoder}$.Our method requires a very small seed lexicon and a large raw corpus. We assume that we can automatically extract discourse-tagged event pairs, $(x_{i1}, x_{i2})$ ($i=1, \cdots $) from the raw corpus. We refer to $x_{i1}$ and $x_{i2}$ as former and latter events, respectively. As shown in Figure FIGREF1, we limit our scope to two discourse relations: Cause and Concession.The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types.The seed lexicon matches (1) the latter event but (2) not the former event, and (3) their discourse relation type is Cause or Concession. If the discourse relation type is Cause, the former event is given the same score as the latter. Likewise, if the discourse relation type is Concession, the former event is given the opposite of the latter's score. They are used as reference scores during training.The seed lexicon matches neither the former nor the latter event, and their discourse relation type is Cause. We assume the two events have the same polarities.The seed lexicon matches neither the former nor the latter event, and their discourse relation type is Concession. We assume the two events have the reversed polarities.Using AL, CA, and CO data, we optimize the parameters of the polarity function $p(x)$. We define a loss function for each of the three types of event pairs and sum up the multiple loss functions.We use mean squared error to construct loss functions. For the AL data, the loss function is defined as:where $x_{i1}$ and $x_{i2}$ are the $i$-th pair of the AL data. $r_{i1}$ and $r_{i2}$ are the automatically-assigned scores of $x_{i1}$ and $x_{i2}$, respectively. $N_{\rm AL}$ is the total number of AL pairs, and $\lambda _{\rm AL}$ is a hyperparameter.For the CA data, the loss function is defined as:$y_{i1}$ and $y_{i2}$ are the $i$-th pair of the CA pairs. $N_{\rm CA}$ is the total number of CA pairs. $\lambda _{\rm CA}$ and $\mu $ are hyperparameters. The first term makes the scores of the two events closer while the second term prevents the scores from shrinking to zero.The loss function for the CO data is defined analogously:The difference is that the first term makes the scores of the two events distant from each other.As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as “ので” (because) and “のに” (in spite of) were present. We treated Cause/Reason (原因・理由) and Condition (条件) in the original tagset BIBREF15 as Cause and Concession (逆接) as Concession, respectively. Here is an example of event pair extraction.. 重大な失敗を犯したので、仕事をクビになった。Because [I] made a serious mistake, [I] got fired.From this sentence, we extracted the event pair of “重大な失敗を犯す” ([I] make a serious mistake) and “仕事をクビになる” ([I] get fired), and tagged it with Cause.We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16.We used the latest version of the ACP Corpus BIBREF12 for evaluation. It was used for (semi-)supervised training as well. Extracted from Japanese websites using HTML layouts and linguistic patterns, the dataset covered various genres. For example, the following two sentences were labeled positive and negative, respectively:. 作業が楽だ。The work is easy.. 駐車場がない。There is no parking lot.Although the ACP corpus was originally constructed in the context of sentiment analysis, we found that it could roughly be regarded as a collection of affective events. We parsed each sentence and extracted the last clause in it. The train/dev/test split of the data is shown in Table TABREF19.The objective function for supervised training is:where $v_i$ is the $i$-th event, $R_i$ is the reference score of $v_i$, and $N_{\rm ACP}$ is the number of the events of the ACP Corpus.To optimize the hyperparameters, we used the dev set of the ACP Corpus. For the evaluation, we used the test set of the ACP Corpus. The model output was classified as positive if $p(x) > 0$ and negative if $p(x) \le 0$.As for ${\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. GRU BIBREF16 is a recurrent neural network sequence encoder. BiGRU reads an input sequence forward and backward and the output is the concatenation of the final forward and backward hidden states.BERT BIBREF17 is a pre-trained multi-layer bidirectional Transformer BIBREF18 encoder. Its output is the final hidden state corresponding to the special classification tag ([CLS]). For the details of ${\rm Encoder}$, see Sections SECREF30.We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\mathcal {L}_{\rm AL}$, $\mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$, $\mathcal {L}_{\rm ACP}$, and $\mathcal {L}_{\rm ACP} + \mathcal {L}_{\rm AL} + \mathcal {L}_{\rm CA} + \mathcal {L}_{\rm CO}$.Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in the seed lexicon. We can see that the seed lexicon itself had practically no impact on prediction.The models in the top block performed considerably better than the random baselines. The performance gaps with their (semi-)supervised counterparts, shown in the middle block, were less than 7%. This demonstrates the effectiveness of discourse relation-based label propagation.Comparing the model variants, we obtained the highest score with the BiGRU encoder trained with the AL+CA+CO dataset. BERT was competitive but its performance went down if CA and CO were used in addition to AL. We conjecture that BERT was more sensitive to noises found more frequently in CA and CO.Contrary to our expectations, supervised models (ACP) outperformed semi-supervised models (ACP+AL+CA+CO). This suggests that the training set of 0.6 million events is sufficiently large for training the models. For comparison, we trained the models with a subset (6,000 events) of the ACP dataset. As the results shown in Table TABREF24 demonstrate, our method is effective when labeled data are small.The result of hyperparameter optimization for the BiGRU encoder was as follows:As the CA and CO pairs were equal in size (Table TABREF16), $\lambda _{\rm CA}$ and $\lambda _{\rm CO}$ were comparable values. $\lambda _{\rm CA}$ was about one-third of $\lambda _{\rm CO}$, and this indicated that the CA pairs were noisier than the CO pairs. A major type of CA pairs that violates our assumption was in the form of “$\textit {problem}_{\text{negative}}$ causes $\textit {solution}_{\text{positive}}$”:. (悪いところがある, よくなるように努力する)(there is a bad point, [I] try to improve [it])The polarities of the two events were reversed in spite of the Cause relation, and this lowered the value of $\lambda _{\rm CA}$.Some examples of model outputs are shown in Table TABREF26. The first two examples suggest that our model successfully learned negation without explicit supervision. Similarly, the next two examples differ only in voice but the model correctly recognized that they had opposite polarities. The last two examples share the predicate “落とす" (drop) and only the objects are different. The second event “肩を落とす" (lit. drop one's shoulders) is an idiom that expresses a disappointed feeling. The examples demonstrate that our model correctly learned non-compositional expressions.In this paper, we proposed to use discourse relations to effectively propagate polarities of affective events from seeds. Experiments show that, even with a minimal amount of supervision, the proposed method performed well.Although event pairs linked by discourse analysis are shown to be useful, they nevertheless contain noises. Adding linguistically-motivated filtering rules would help improve the performance.We thank Nobuhiro Kaji for providing the ACP Corpus and Hirokazu Kiyomaru and Yudai Kishimoto for their help in extracting event pairs. This work was partially supported by Yahoo! Japan Corporation.喜ぶ (rejoice), 嬉しい (be glad), 楽しい (be pleasant), 幸せ (be happy), 感動 (be impressed), 興奮 (be excited), 懐かしい (feel nostalgic), 好き (like), 尊敬 (respect), 安心 (be relieved), 感心 (admire), 落ち着く (be calm), 満足 (be satisfied), 癒される (be healed), and スッキリ (be refreshed).怒る (get angry), 悲しい (be sad), 寂しい (be lonely), 怖い (be scared), 不安 (feel anxious), 恥ずかしい (be embarrassed), 嫌 (hate), 落ち込む (feel down), 退屈 (be bored), 絶望 (feel hopeless), 辛い (have a hard time), 困る (have trouble), 憂鬱 (be depressed), 心配 (be worried), and 情けない (be sorry).The dimension of the embedding layer was 256. The embedding layer was initialized with the word embeddings pretrained using the Web corpus. The input sentences were segmented into words by the morphological analyzer Juman++. The vocabulary size was 100,000. The number of hidden layers was 2. The dimension of hidden units was 256. The optimizer was Momentum SGD BIBREF21. The mini-batch size was 1024. We ran 100 epochs and selected the snapshot that achieved the highest score for the dev set.We used a Japanese BERT model pretrained with Japanese Wikipedia. The input sentences were segmented into words by Juman++, and words were broken into subwords by applying BPE BIBREF20. The vocabulary size was 32,000. The maximum length of an input sequence was 128. The number of hidden layers was 12. The dimension of hidden units was 768. The number of self-attention heads was 12. The optimizer was Adam BIBREF19. The mini-batch size was 32. We ran 1 epoch. | [
"What is the seed lexicon?",
"What are the results?",
"How are relations used to propagate polarity?",
"How big is the Japanese data?",
"What are labels available in dataset for supervision?",
"How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?",
"How does their model learn using mostly raw data?",
"How big is seed lexicon used for training?",
"How large is raw corpus used for training?"
] | [
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"a vocabulary of positive and negative predicates that helps determine the polarity score of an event",
""
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"Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -- BERT achieved 0.913 accuracy. \nUsing a subset to train: BERT achieved 0.876 accuracy using ACP (6K), BERT achieved 0.886 accuracy using ACP (6K) + AL, BiGRU achieved 0.830 accuracy using ACP (6K), BiGRU achieved 0.879 accuracy using ACP (6K) + AL + CA + CO."
],
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"based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event ",
"cause relation: both events in the relation should have the same polarity; concession relation: events should have opposite polarity"
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"7000000 pairs of events were extracted from the Japanese Web corpus, 529850 pairs of events were extracted from the ACP corpus",
"The ACP corpus has around 700k events split into positive and negative polarity "
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""
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"3%"
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"by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity"
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"30 words"
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1.1em1.1.1em1.1.1.1emThomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny Kim$^3$, Roman Klinger$^3$, Winfried Menninghaus$^1$$^{1}$Department of Language and Literature, Max Planck Institute for Empirical Aesthetics$^{2}$NLLG, Department of Computer Science, Technische Universitat Darmstadt$^{3}$Institut für Maschinelle Sprachverarbeitung, University of Stuttgart{thomas.haider, w.m}@ae.mpg.de, [email protected]{roman.klinger, evgeny.kim}@ims.uni-stuttgart.deMost approaches to emotion analysis regarding social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions that have been shown to also include mixed emotional responses. We consider emotions as they are elicited in the reader, rather than what is expressed in the text or intended by the author. Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within context. We evaluate this novel setting in an annotation experiment both with carefully trained experts and via crowdsourcing. Our annotation with experts leads to an acceptable agreement of $\kappa =.70$, resulting in a consistent dataset for future large scale analysis. Finally, we conduct first emotion classification experiments based on BERT, showing that identifying aesthetic emotions is challenging in our data, with up to .52 F1-micro on the German subset. Data and resources are available at https://github.com/tnhaider/poetry-emotion.Emotion, Aesthetic Emotions, Literature, Poetry, Annotation, Corpora, Emotion Recognition, Multi-LabelEmotions are central to human experience, creativity and behavior. Models of affect and emotion, both in psychology and natural language processing, commonly operate on predefined categories, designated either by continuous scales of, e.g., Valence, Arousal and Dominance BIBREF0 or discrete emotion labels (which can also vary in intensity). Discrete sets of emotions often have been motivated by theories of basic emotions, as proposed by Ekman1992—Anger, Fear, Joy, Disgust, Surprise, Sadness—and Plutchik1991, who added Trust and Anticipation. These categories are likely to have evolved as they motivate behavior that is directly relevant for survival. However, art reception typically presupposes a situation of safety and therefore offers special opportunities to engage in a broader range of more complex and subtle emotions. These differences between real-life and art contexts have not been considered in natural language processing work so far.To emotionally move readers is considered a prime goal of literature since Latin antiquity BIBREF1, BIBREF2, BIBREF3. Deeply moved readers shed tears or get chills and goosebumps even in lab settings BIBREF4. In cases like these, the emotional response actually implies an aesthetic evaluation: narratives that have the capacity to move readers are evaluated as good and powerful texts for this very reason. Similarly, feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking). Emotions that exhibit this dual capacity have been defined as “aesthetic emotions” BIBREF2. Contrary to the negativity bias of classical emotion catalogues, emotion terms used for aesthetic evaluation purposes include far more positive than negative emotions. At the same time, many overall positive aesthetic emotions encompass negative or mixed emotional ingredients BIBREF2, e.g., feelings of suspense include both hopeful and fearful anticipations.For these reasons, we argue that the analysis of literature (with a focus on poetry) should rely on specifically selected emotion items rather than on the narrow range of basic emotions only. Our selection is based on previous research on this issue in psychological studies on art reception and, specifically, on poetry. For instance, knoop2016mapping found that Beauty is a major factor in poetry reception.We primarily adopt and adapt emotion terms that schindler2017measuring have identified as aesthetic emotions in their study on how to measure and categorize such particular affective states. Further, we consider the aspect that, when selecting specific emotion labels, the perspective of annotators plays a major role. Whether emotions are elicited in the reader, expressed in the text, or intended by the author largely changes the permissible labels. For example, feelings of Disgust or Love might be intended or expressed in the text, but the text might still fail to elicit corresponding feelings as these concepts presume a strong reaction in the reader. Our focus here was on the actual emotional experience of the readers rather than on hypothetical intentions of authors. We opted for this reader perspective based on previous research in NLP BIBREF5, BIBREF6 and work in empirical aesthetics BIBREF7, that specifically measured the reception of poetry. Our final set of emotion labels consists of Beauty/Joy, Sadness, Uneasiness, Vitality, Suspense, Awe/Sublime, Humor, Annoyance, and Nostalgia.In addition to selecting an adapted set of emotions, the annotation of poetry brings further challenges, one of which is the choice of the appropriate unit of annotation. Previous work considers words BIBREF8, BIBREF9, sentences BIBREF10, BIBREF11, utterances BIBREF12, sentence triples BIBREF13, or paragraphs BIBREF14 as the units of annotation. For poetry, reasonable units follow the logical document structure of poems, i.e., verse (line), stanza, and, owing to its relative shortness, the complete text. The more coarse-grained the unit, the more difficult the annotation is likely to be, but the more it may also enable the annotation of emotions in context. We find that annotating fine-grained units (lines) that are hierarchically ordered within a larger context (stanza, poem) caters to the specific structure of poems, where emotions are regularly mixed and are more interpretable within the whole poem. Consequently, we allow the mixing of emotions already at line level through multi-label annotation.The remainder of this paper includes (1) a report of the annotation process that takes these challenges into consideration, (2) a description of our annotated corpora, and (3) an implementation of baseline models for the novel task of aesthetic emotion annotation in poetry. In a first study, the annotators work on the annotations in a closely supervised fashion, carefully reading each verse, stanza, and poem. In a second study, the annotations are performed via crowdsourcing within relatively short time periods with annotators not seeing the entire poem while reading the stanza. Using these two settings, we aim at obtaining a better understanding of the advantages and disadvantages of an expert vs. crowdsourcing setting in this novel annotation task. Particularly, we are interested in estimating the potential of a crowdsourcing environment for the task of self-perceived emotion annotation in poetry, given time and cost overhead associated with in-house annotation process (that usually involve training and close supervision of the annotators).We provide the final datasets of German and English language poems annotated with reader emotions on verse level at https://github.com/tnhaider/poetry-emotion.Natural language understanding research on poetry has investigated stylistic variation BIBREF15, BIBREF16, BIBREF17, with a focus on broadly accepted formal features such as meter BIBREF18, BIBREF19, BIBREF20 and rhyme BIBREF21, BIBREF22, as well as enjambement BIBREF23, BIBREF24 and metaphor BIBREF25, BIBREF26. Recent work has also explored the relationship of poetry and prose, mainly on a syntactic level BIBREF27, BIBREF28. Furthermore, poetry also lends itself well to semantic (change) analysis BIBREF29, BIBREF30, as linguistic invention BIBREF31, BIBREF32 and succinctness BIBREF33 are at the core of poetic production.Corpus-based analysis of emotions in poetry has been considered, but there is no work on German, and little on English. kao2015computational analyze English poems with word associations from the Harvard Inquirer and LIWC, within the categories positive/negative outlook, positive/negative emotion and phys./psych. well-being. hou-frank-2015-analyzing examine the binary sentiment polarity of Chinese poems with a weighted personalized PageRank algorithm. barros2013automatic followed a tagging approach with a thesaurus to annotate words that are similar to the words `Joy', `Anger', `Fear' and `Sadness' (moreover translating these from English to Spanish). With these word lists, they distinguish the categories `Love', `Songs to Lisi', `Satire' and `Philosophical-Moral-Religious' in Quevedo's poetry. Similarly, alsharif2013emotion classify unique Arabic `emotional text forms' based on word unigrams.Mohanty2018 create a corpus of 788 poems in the Indian Odia language, annotate it on text (poem) level with binary negative and positive sentiment, and are able to distinguish these with moderate success. Sreeja2019 construct a corpus of 736 Indian language poems and annotate the texts on Ekman's six categories + Love + Courage. They achieve a Fleiss Kappa of .48.In contrast to our work, these studies focus on basic emotions and binary sentiment polarity only, rather than addressing aesthetic emotions. Moreover, they annotate on the level of complete poems (instead of fine-grained verse and stanza-level).Emotion corpora have been created for different tasks and with different annotation strategies, with different units of analysis and different foci of emotion perspective (reader, writer, text). Examples include the ISEAR dataset BIBREF34 (document-level); emotion annotation in children stories BIBREF10 and news headlines BIBREF35 (sentence-level); and fine-grained emotion annotation in literature by Kim2018 (phrase- and word-level). We refer the interested reader to an overview paper on existing corpora BIBREF36.We are only aware of a limited number of publications which look in more depth into the emotion perspective. buechel-hahn-2017-emobank report on an annotation study that focuses both on writer's and reader's emotions associated with English sentences. The results show that the reader perspective yields better inter-annotator agreement. Yang2009 also study the difference between writer and reader emotions, but not with a modeling perspective. The authors find that positive reader emotions tend to be linked to positive writer emotions in online blogs.The task of emotion classification has been tackled before using rule-based and machine learning approaches. Rule-based emotion classification typically relies on lexical resources of emotionally charged words BIBREF9, BIBREF37, BIBREF8 and offers a straightforward and transparent way to detect emotions in text.In contrast to rule-based approaches, current models for emotion classification are often based on neural networks and commonly use word embeddings as features. Schuff2017 applied models from the classes of CNN, BiLSTM, and LSTM and compare them to linear classifiers (SVM and MaxEnt), where the BiLSTM shows best results with the most balanced precision and recall. AbdulMageed2017 claim the highest F$_1$ with gated recurrent unit networks BIBREF38 for Plutchik's emotion model. More recently, shared tasks on emotion analysis BIBREF39, BIBREF40 triggered a set of more advanced deep learning approaches, including BERT BIBREF41 and other transfer learning methods BIBREF42.For our annotation and modeling studies, we build on top of two poetry corpora (in English and German), which we refer to as PO-EMO. This collection represents important contributions to the literary canon over the last 400 years. We make this resource available in TEI P5 XML and an easy-to-use tab separated format. Table TABREF9 shows a size overview of these data sets. Figure FIGREF8 shows the distribution of our data over time via density plots. Note that both corpora show a relative underrepresentation before the onset of the romantic period (around 1750).The German corpus contains poems available from the website lyrik.antikoerperchen.de (ANTI-K), which provides a platform for students to upload essays about poems. The data is available in the Hypertext Markup Language, with clean line and stanza segmentation. ANTI-K also has extensive metadata, including author names, years of publication, numbers of sentences, poetic genres, and literary periods, that enable us to gauge the distribution of poems according to periods. The 158 poems we consider (731 stanzas) are dispersed over 51 authors and the New High German timeline (1575–1936 A.D.). This data has been annotated, besides emotions, for meter, rhythm, and rhyme in other studies BIBREF22, BIBREF43.The English corpus contains 64 poems of popular English writers. It was partly collected from Project Gutenberg with the GutenTag tool, and, in addition, includes a number of hand selected poems from the modern period and represents a cross section of popular English poets. We took care to include a number of female authors, who would have been underrepresented in a uniform sample. Time stamps in the corpus are organized by the birth year of the author, as assigned in Project Gutenberg.In the following, we will explain how we compiled and annotated three data subsets, namely, (1) 48 German poems with gold annotation. These were originally annotated by three annotators. The labels were then aggregated with majority voting and based on discussions among the annotators. Finally, they were curated to only include one gold annotation. (2) The remaining 110 German poems that are used to compute the agreement in table TABREF20 and (3) 64 English poems contain the raw annotation from two annotators.We report the genesis of our annotation guidelines including the emotion classes. With the intention to provide a language resource for the computational analysis of emotion in poetry, we aimed at maximizing the consistency of our annotation, while doing justice to the diversity of poetry. We iteratively improved the guidelines and the annotation workflow by annotating in batches, cleaning the class set, and the compilation of a gold standard. The final overall cost of producing this expert annotated dataset amounts to approximately 3,500.The annotation process was initially conducted by three female university students majoring in linguistics and/or literary studies, which we refer to as our “expert annotators”. We used the INCePTION platform for annotation BIBREF44. Starting with the German poems, we annotated in batches of about 16 (and later in some cases 32) poems. After each batch, we computed agreement statistics including heatmaps, and provided this feedback to the annotators. For the first three batches, the three annotators produced a gold standard using a majority vote for each line. Where this was inconclusive, they developed an adjudicated annotation based on discussion. Where necessary, we encouraged the annotators to aim for more consistency, as most of the frequent switching of emotions within a stanza could not be reconstructed or justified.In poems, emotions are regularly mixed (already on line level) and are more interpretable within the whole poem. We therefore annotate lines hierarchically within the larger context of stanzas and the whole poem. Hence, we instruct the annotators to read a complete stanza or full poem, and then annotate each line in the context of its stanza. To reflect on the emotional complexity of poetry, we allow a maximum of two labels per line while avoiding heavy label fluctuations by encouraging annotators to reflect on their feelings to avoid `empty' annotations. Rather, they were advised to use fewer labels and more consistent annotation. This additional constraint is necessary to avoid “wild”, non-reconstructable or non-justified annotations.All subsequent batches (all except the first three) were only annotated by two out of the three initial annotators, coincidentally those two who had the lowest initial agreement with each other. We asked these two experts to use the generated gold standard (48 poems; majority votes of 3 annotators plus manual curation) as a reference (“if in doubt, annotate according to the gold standard”). This eliminated some systematic differences between them and markedly improved the agreement levels, roughly from 0.3–0.5 Cohen's $\kappa $ in the first three batches to around 0.6–0.8 $\kappa $ for all subsequent batches. This annotation procedure relaxes the reader perspective, as we encourage annotators (if in doubt) to annotate how they think the other annotators would annotate. However, we found that this formulation improves the usability of the data and leads to a more consistent annotation.We opt for measuring the reader perspective rather than the text surface or author's intent. To closer define and support conceptualizing our labels, we use particular `items', as they are used in psychological self-evaluations. These items consist of adjectives, verbs or short phrases. We build on top of schindler2017measuring who proposed 43 items that were then grouped by a factor analysis based on self-evaluations of participants. The resulting factors are shown in Table TABREF17. We attempt to cover all identified factors and supplement with basic emotions BIBREF46, BIBREF47, where possible.We started with a larger set of labels to then delete and substitute (tone down) labels during the initial annotation process to avoid infrequent classes and inconsistencies. Further, we conflate labels if they show considerable confusion with each other. These iterative improvements particularly affected Confusion, Boredom and Other that were very infrequently annotated and had little agreement among annotators ($\kappa <.2$). For German, we also removed Nostalgia ($\kappa =.218$) after gold standard creation, but after consideration, added it back for English, then achieving agreement. Nostalgia is still available in the gold standard (then with a second label Beauty/Joy or Sadness to keep consistency). However, Confusion, Boredom and Other are not available in any sub-corpus.Our final set consists of nine classes, i.e., (in order of frequency) Beauty/Joy, Sadness, Uneasiness, Vitality, Suspense, Awe/Sublime, Humor, Annoyance, and Nostalgia. In the following, we describe the labels and give further details on the aggregation process.Annoyance (annoys me/angers me/felt frustrated): Annoyance implies feeling annoyed, frustrated or even angry while reading the line/stanza. We include the class Anger here, as this was found to be too strong in intensity.Awe/Sublime (found it overwhelming/sense of greatness): Awe/Sublime implies being overwhelmed by the line/stanza, i.e., if one gets the impression of facing something sublime or if the line/stanza inspires one with awe (or that the expression itself is sublime). Such emotions are often associated with subjects like god, death, life, truth, etc. The term Sublime originated with kant2000critique as one of the first aesthetic emotion terms. Awe is a more common English term.Beauty/Joy (found it beautiful/pleasing/makes me happy/joyful): kant2000critique already spoke of a “feeling of beauty”, and it should be noted that it is not a `merely pleasing emotion'. Therefore, in our pilot annotations, Beauty and Joy were separate labels. However, schindler2017measuring found that items for Beauty and Joy load into the same factors. Furthermore, our pilot annotations revealed, while Beauty is the more dominant and frequent feeling, both labels regularly accompany each other, and they often get confused across annotators. Therefore, we add Joy to form an inclusive label Beauty/Joy that increases annotation consistency.Humor (found it funny/amusing): Implies feeling amused by the line/stanza or if it makes one laugh.Nostalgia (makes me nostalgic): Nostalgia is defined as a sentimental longing for things, persons or situations in the past. It often carries both positive and negative feelings. However, since this label is quite infrequent, and not available in all subsets of the data, we annotated it with an additional Beauty/Joy or Sadness label to ensure annotation consistency.Sadness (makes me sad/touches me): If the line/stanza makes one feel sad. It also includes a more general `being touched / moved'.Suspense (found it gripping/sparked my interest): Choose Suspense if the line/stanza keeps one in suspense (if the line/stanza excites one or triggers one's curiosity). We further removed Anticipation from Suspense/Anticipation, as Anticipation appeared to us as being a more cognitive prediction whereas Suspense is a far more straightforward emotion item.Uneasiness (found it ugly/unsettling/disturbing / frightening/distasteful): This label covers situations when one feels discomfort about the line/stanza (if the line/stanza feels distasteful/ugly, unsettling/disturbing or frightens one). The labels Ugliness and Disgust were conflated into Uneasiness, as both are seldom felt in poetry (being inadequate/too strong/high in arousal), and typically lead to Uneasiness.Vitality (found it invigorating/spurs me on/inspires me): This label is meant for a line/stanza that has an inciting, encouraging effect (if the line/stanza conveys a feeling of movement, energy and vitality which animates to action). Similar terms are Activation and Stimulation.Table TABREF20 shows the Cohen's $\kappa $ agreement scores among our two expert annotators for each emotion category $e$ as follows. We assign each instance (a line in a poem) a binary label indicating whether or not the annotator has annotated the emotion category $e$ in question. From this, we obtain vectors $v_i^e$, for annotators $i=0,1$, where each entry of $v_i^e$ holds the binary value for the corresponding line. We then apply the $\kappa $ statistics to the two binary vectors $v_i^e$. Additionally to averaged $\kappa $, we report micro-F1 values in Table TABREF21 between the multi-label annotations of both expert annotators as well as the micro-F1 score of a random baseline as well as of the majority emotion baseline (which labels each line as Beauty/Joy).We find that Cohen $\kappa $ agreement ranges from .84 for Uneasiness in the English data, .81 for Humor and Nostalgia, down to German Suspense (.65), Awe/Sublime (.61) and Vitality for both languages (.50 English, .63 German). Both annotators have a similar emotion frequency profile, where the ranking is almost identical, especially for German. However, for English, Annotator 2 annotates more Vitality than Uneasiness. Figure FIGREF18 shows the confusion matrices of labels between annotators as heatmaps. Notably, Beauty/Joy and Sadness are confused across annotators more often than other labels. This is topical for poetry, and therefore not surprising: One might argue that the beauty of beings and situations is only beautiful because it is not enduring and therefore not to divorce from the sadness of the vanishing of beauty BIBREF48. We also find considerable confusion of Sadness with Awe/Sublime and Vitality, while the latter is also regularly confused with Beauty/Joy.Furthermore, as shown in Figure FIGREF23, we find that no single poem aggregates to more than six emotion labels, while no stanza aggregates to more than four emotion labels. However, most lines and stanzas prefer one or two labels. German poems seem more emotionally diverse where more poems have three labels than two labels, while the majority of English poems have only two labels. This is however attributable to the generally shorter English texts.After concluding the expert annotation, we performed a focused crowdsourcing experiment, based on the final label set and items as they are listed in Table TABREF27 and Section SECREF19. With this experiment, we aim to understand whether it is possible to collect reliable judgements for aesthetic perception of poetry from a crowdsourcing platform. A second goal is to see whether we can replicate the expensive expert annotations with less costly crowd annotations.We opted for a maximally simple annotation environment, where we asked participants to annotate English 4-line stanzas with self-perceived reader emotions. We choose English due to the higher availability of English language annotators on crowdsourcing platforms. Each annotator rates each stanza independently of surrounding context.For consistency and to simplify the task for the annotators, we opt for a trade-off between completeness and granularity of the annotation. Specifically, we subselect stanzas composed of four verses from the corpus of 64 hand selected English poems. The resulting selection of 59 stanzas is uploaded to Figure Eight for annotation.The annotators are asked to answer the following questions for each instance.Question 1 (single-choice): Read the following stanza and decide for yourself which emotions it evokes.Question 2 (multiple-choice): Which additional emotions does the stanza evoke?The answers to both questions correspond to the emotion labels we defined to use in our annotation, as described in Section SECREF19. We add an additional answer choice “None” to Question 2 to allow annotators to say that a stanza does not evoke any additional emotions.Each instance is annotated by ten people. We restrict the task geographically to the United Kingdom and Ireland and set the internal parameters on Figure Eight to only include the highest quality annotators to join the task. We pay 0.09 per instance. The final cost of the crowdsourcing experiment is 74.In the following, we determine the best aggregation strategy regarding the 10 annotators with bootstrap resampling. For instance, one could assign the label of a specific emotion to an instance if just one annotators picks it, or one could assign the label only if all annotators agree on this emotion. To evaluate this, we repeatedly pick two sets of 5 annotators each out of the 10 annotators for each of the 59 stanzas, 1000 times overall (i.e., 1000$\times $59 times, bootstrap resampling). For each of these repetitions, we compare the agreement of these two groups of 5 annotators. Each group gets assigned with an adjudicated emotion which is accepted if at least one annotator picks it, at least two annotators pick it, etc. up to all five pick it.We show the results in Table TABREF27. The $\kappa $ scores show the average agreement between the two groups of five annotators, when the adjudicated class is picked based on the particular threshold of annotators with the same label choice. We see that some emotions tend to have higher agreement scores than others, namely Annoyance (.66), Sadness (up to .52), and Awe/Sublime, Beauty/Joy, Humor (all .46). The maximum agreement is reached mostly with a threshold of 2 (4 times) or 3 (3 times).We further show in the same table the average numbers of labels from each strategy. Obviously, a lower threshold leads to higher numbers (corresponding to a disjunction of annotations for each emotion). The drop in label counts is comparably drastic, with on average 18 labels per class. Overall, the best average $\kappa $ agreement (.32) is less than half of what we saw for the expert annotators (roughly .70). Crowds especially disagree on many more intricate emotion labels (Uneasiness, Vitality, Nostalgia, Suspense).We visualize how often two emotions are used to label an instance in a confusion table in Figure FIGREF18. Sadness is used most often to annotate a stanza, and it is often confused with Suspense, Uneasiness, and Nostalgia. Further, Beauty/Joy partially overlaps with Awe/Sublime, Nostalgia, and Sadness.On average, each crowd annotator uses two emotion labels per stanza (56% of cases); only in 36% of the cases the annotators use one label, and in 6% and 1% of the cases three and four labels, respectively. This contrasts with the expert annotators, who use one label in about 70% of the cases and two labels in 30% of the cases for the same 59 four-liners. Concerning frequency distribution for emotion labels, both experts and crowds name Sadness and Beauty/Joy as the most frequent emotions (for the `best' threshold of 3) and Nostalgia as one of the least frequent emotions. The Spearman rank correlation between experts and crowds is about 0.55 with respect to the label frequency distribution, indicating that crowds could replace experts to a moderate degree when it comes to extracting, e.g., emotion distributions for an author or time period. Now, we further compare crowds and experts in terms of whether crowds could replicate expert annotations also on a finer stanza level (rather than only on a distributional level).To gauge the quality of the crowd annotations in comparison with our experts, we calculate agreement on the emotions between experts and an increasing group size from the crowd. For each stanza instance $s$, we pick $N$ crowd workers, where $N\in \lbrace 4,6,8,10\rbrace $, then pick their majority emotion for $s$, and additionally pick their second ranked majority emotion if at least $\frac{N}{2}-1$ workers have chosen it. For the experts, we aggregate their emotion labels on stanza level, then perform the same strategy for selection of emotion labels. Thus, for $s$, both crowds and experts have 1 or 2 emotions. For each emotion, we then compute Cohen's $\kappa $ as before. Note that, compared to our previous experiments in Section SECREF26 with a threshold, each stanza now receives an emotion annotation (exactly one or two emotion labels), both by the experts and the crowd-workers.In Figure FIGREF30, we plot agreement between experts and crowds on stanza level as we vary the number $N$ of crowd workers involved. On average, there is roughly a steady linear increase in agreement as $N$ grows, which may indicate that $N=20$ or $N=30$ would still lead to better agreement. Concerning individual emotions, Nostalgia is the emotion with the least agreement, as opposed to Sadness (in our sample of 59 four-liners): the agreement for this emotion grows from $.47$ $\kappa $ with $N=4$ to $.65$ $\kappa $ with $N=10$. Sadness is also the most frequent emotion, both according to experts and crowds. Other emotions for which a reasonable agreement is achieved are Annoyance, Awe/Sublime, Beauty/Joy, Humor ($\kappa $ > 0.2). Emotions with little agreement are Vitality, Uneasiness, Suspense, Nostalgia ($\kappa $ < 0.2).By and large, we note from Figure FIGREF18 that expert annotation is more restrictive, with experts agreeing more often on particular emotion labels (seen in the darker diagonal). The results of the crowdsourcing experiment, on the other hand, are a mixed bag as evidenced by a much sparser distribution of emotion labels. However, we note that these differences can be caused by 1) the disparate training procedure for the experts and crowds, and 2) the lack of opportunities for close supervision and on-going training of the crowds, as opposed to the in-house expert annotators.In general, however, we find that substituting experts with crowds is possible to a certain degree. Even though the crowds' labels look inconsistent at first view, there appears to be a good signal in their aggregated annotations, helping to approximate expert annotations to a certain degree. The average $\kappa $ agreement (with the experts) we get from $N=10$ crowd workers (0.24) is still considerably below the agreement among the experts (0.70).To estimate the difficulty of automatic classification of our data set, we perform multi-label document classification (of stanzas) with BERT BIBREF41. For this experiment we aggregate all labels for a stanza and sort them by frequency, both for the gold standard and the raw expert annotations. As can be seen in Figure FIGREF23, a stanza bears a minimum of one and a maximum of four emotions. Unfortunately, the label Nostalgia is only available 16 times in the German data (the gold standard) as a second label (as discussed in Section SECREF19). None of our models was able to learn this label for German. Therefore we omit it, leaving us with eight proper labels.We use the code and the pre-trained BERT models of Farm, provided by deepset.ai. We test the multilingual-uncased model (Multiling), the german-base-cased model (Base), the german-dbmdz-uncased model (Dbmdz), and we tune the Base model on 80k stanzas of the German Poetry Corpus DLK BIBREF30 for 2 epochs, both on token (masked words) and sequence (next line) prediction (Base$_{\textsc {Tuned}}$).We split the randomized German dataset so that each label is at least 10 times in the validation set (63 instances, 113 labels), and at least 10 times in the test set (56 instances, 108 labels) and leave the rest for training (617 instances, 946 labels). We train BERT for 10 epochs (with a batch size of 8), optimize with entropy loss, and report F1-micro on the test set. See Table TABREF36 for the results.We find that the multilingual model cannot handle infrequent categories, i.e., Awe/Sublime, Suspense and Humor. However, increasing the dataset with English data improves the results, suggesting that the classification would largely benefit from more annotated data. The best model overall is DBMDZ (.520), showing a balanced response on both validation and test set. See Table TABREF37 for a breakdown of all emotions as predicted by the this model. Precision is mostly higher than recall. The labels Awe/Sublime, Suspense and Humor are harder to predict than the other labels.The BASE and BASE$_{\textsc {TUNED}}$ models perform slightly worse than DBMDZ. The effect of tuning of the BASE model is questionable, probably because of the restricted vocabulary (30k). We found that tuning on poetry does not show obvious improvements. Lastly, we find that models that were trained on lines (instead of stanzas) do not achieve the same F1 (~.42 for the German models).In this paper, we presented a dataset of German and English poetry annotated with reader response to reading poetry. We argued that basic emotions as proposed by psychologists (such as Ekman and Plutchik) that are often used in emotion analysis from text are of little use for the annotation of poetry reception. We instead conceptualized aesthetic emotion labels and showed that a closely supervised annotation task results in substantial agreement—in terms of $\kappa $ score—on the final dataset.The task of collecting reader-perceived emotion response to poetry in a crowdsourcing setting is not straightforward. In contrast to expert annotators, who were closely supervised and reflected upon the task, the annotators on crowdsourcing platforms are difficult to control and may lack necessary background knowledge to perform the task at hand. However, using a larger number of crowd annotators may lead to finding an aggregation strategy with a better trade-off between quality and quantity of adjudicated labels. For future work, we thus propose to repeat the experiment with larger number of crowdworkers, and develop an improved training strategy that would suit the crowdsourcing environment.The dataset presented in this paper can be of use for different application scenarios, including multi-label emotion classification, style-conditioned poetry generation, investigating the influence of rhythm/prosodic features on emotion, or analysis of authors, genres and diachronic variation (e.g., how emotions are represented differently in certain periods).Further, though our modeling experiments are still rudimentary, we propose that this data set can be used to investigate the intra-poem relations either through multi-task learning BIBREF49 and/or with the help of hierarchical sequence classification approaches.A special thanks goes to Gesine Fuhrmann, who created the guidelines and tirelessly documented the annotation progress. Also thanks to Annika Palm and Debby Trzeciak who annotated and gave lively feedback. For help with the conceptualization of labels we thank Ines Schindler. This research has been partially conducted within the CRETA center (http://www.creta.uni-stuttgart.de/) which is funded by the German Ministry for Education and Research (BMBF) and partially funded by the German Research Council (DFG), projects SEAT (Structured Multi-Domain Emotion Analysis from Text, KL 2869/1-1). This work has also been supported by the German Research Foundation as part of the Research Training Group Adaptive Preparation of Information from Heterogeneous Sources (AIPHES) at the Technische Universität Darmstadt under grant No. GRK 1994/1.We illustrate two examples of our German gold standard annotation, a poem each by Friedrich Hölderlin and Georg Trakl, and an English poem by Walt Whitman. Hölderlin's text stands out, because the mood changes starkly from the first stanza to the second, from Beauty/Joy to Sadness. Trakl's text is a bit more complex with bits of Nostalgia and, most importantly, a mixture of Uneasiness with Awe/Sublime. Whitman's poem is an example of Vitality and its mixing with Sadness. The English annotation was unified by us for space constraints. For the full annotation please see https://github.com/tnhaider/poetry-emotion/ | [
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“If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.”— Italo Calvino, Invisible CitiesA community's identity—defined through the common interests and shared experiences of its users—shapes various facets of the social dynamics within it BIBREF0 , BIBREF1 , BIBREF2 . Numerous instances of this interplay between a community's identity and social dynamics have been extensively studied in the context of individual online communities BIBREF3 , BIBREF4 , BIBREF5 . However, the sheer variety of online platforms complicates the task of generalizing insights beyond these isolated, single-community glimpses. A new way to reason about the variation across multiple communities is needed in order to systematically characterize the relationship between properties of a community and the dynamics taking place within.One especially important component of community dynamics is user engagement. We can aim to understand why users join certain communities BIBREF6 , what factors influence user retention BIBREF7 , and how users react to innovation BIBREF5 . While striking patterns of user engagement have been uncovered in prior case studies of individual communities BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , we do not know whether these observations hold beyond these cases, or when we can draw analogies between different communities. Are there certain types of communities where we can expect similar or contrasting engagement patterns?To address such questions quantitatively we need to provide structure to the diverse and complex space of online communities. Organizing the multi-community landscape would allow us to both characterize individual points within this space, and reason about systematic variations in patterns of user engagement across the space.Present work: Structuring the multi-community space. In order to systematically understand the relationship between community identityand user engagement we introduce a quantitative typology of online communities. Our typology is based on two key aspects of community identity: how distinctive—or niche—a community's interests are relative to other communities, and how dynamic—or volatile—these interests are over time. These axes aim to capture the salience of a community's identity and dynamics of its temporal evolution.Our main insight in implementing this typology automatically and at scale is that the language used within a community can simultaneously capture how distinctive and dynamic its interests are. This language-based approach draws on a wealth of literature characterizing linguistic variation in online communities and its relationship to community and user identity BIBREF16 , BIBREF5 , BIBREF17 , BIBREF18 , BIBREF19 . Basing our typology on language is also convenient since it renders our framework immediately applicable to a wide variety of online communities, where communication is primarily recorded in a textual format.Using our framework, we map almost 300 Reddit communities onto the landscape defined by the two axes of our typology (Section SECREF2 ). We find that this mapping induces conceptually sound categorizations that effectively capture key aspects of community-level social dynamics. In particular, we quantitatively validate the effectiveness of our mapping by showing that our two-dimensional typology encodes signals that are predictive of community-level rates of user retention, complementing strong activity-based features.Engagement and community identity. We apply our framework to understand how two important aspects of user engagement in a community—the community's propensity to retain its users (Section SECREF3 ), and its permeability to new members (Section SECREF4 )—vary according to the type of collective identity it fosters. We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members.More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ). Interestingly, while established members of distinctive communities more avidly respond to temporal updates than newcomers, in more generic communities it is the outsiders who engage more with volatile content, perhaps suggesting that such content may serve as an entry-point to the community (but not necessarily a reason to stay). Such insights into the relation between collective identity and user engagement can be informative to community maintainers seeking to better understand growth patterns within their online communities.More generally, our methodology stands as an example of how sociological questions can be addressed in a multi-community setting. In performing our analyses across a rich variety of communities, we reveal both the diversity of phenomena that can occur, as well as the systematic nature of this diversity.A community's identity derives from its members' common interests and shared experiences BIBREF15 , BIBREF20 . In this work, we structure the multi-community landscape along these two key dimensions of community identity: how distinctive a community's interests are, and how dynamic the community is over time.We now proceed to outline our quantitative typology, which maps communities along these two dimensions. We start by providing an intuition through inspecting a few example communities. We then introduce a generalizable language-based methodology and use it to map a large set of Reddit communities onto the landscape defined by our typology of community identity.In order to illustrate the diversity within the multi-community space, and to provide an intuition for the underlying structure captured by the proposed typology, we first examine a few example communities and draw attention to some key social dynamics that occur within them.We consider four communities from Reddit: in Seahawks, fans of the Seahawks football team gather to discuss games and players; in BabyBumps, expecting mothers trade advice and updates on their pregnancy; Cooking consists of recipe ideas and general discussion about cooking; while in pics, users share various images of random things (like eels and hornets). We note that these communities are topically contrasting and foster fairly disjoint user bases. Additionally, these communities exhibit varied patterns of user engagement. While Seahawks maintains a devoted set of users from month to month, pics is dominated by transient users who post a few times and then depart.Discussions within these communities also span varied sets of interests. Some of these interests are more specific to the community than others: risotto, for example, is seldom a discussion point beyond Cooking. Additionally, some interests consistently recur, while others are specific to a particular time: kitchens are a consistent focus point for cooking, but mint is only in season during spring. Coupling specificity and consistency we find interests such as easter, which isn't particularly specific to BabyBumps but gains prominence in that community around Easter (see Figure FIGREF3 .A for further examples).These specific interests provide a window into the nature of the communities' interests as a whole, and by extension their community identities. Overall, discussions in Cooking focus on topics which are highly distinctive and consistently recur (like risotto). In contrast, discussions in Seahawks are highly dynamic, rapidly shifting over time as new games occur and players are traded in and out. In the remainder of this section we formally introduce a methodology for mapping communities in this space defined by their distinctiveness and dynamicity (examples in Figure FIGREF3 .B).Our approach follows the intuition that a distinctive community will use language that is particularly specific, or unique, to that community. Similarly, a dynamic community will use volatile language that rapidly changes across successive windows of time. To capture this intuition automatically, we start by defining word-level measures of specificity and volatility. We then extend these word-level primitives to characterize entire comments, and the community itself.Our characterizations of words in a community are motivated by methodology from prior literature that compares the frequency of a word in a particular setting to its frequency in some background distribution, in order to identify instances of linguistic variation BIBREF21 , BIBREF19 . Our particular framework makes this comparison by way of pointwise mutual information (PMI).In the following, we use INLINEFORM0 to denote one community within a set INLINEFORM1 of communities, and INLINEFORM2 to denote one time period within the entire history INLINEFORM3 of INLINEFORM4 . We account for temporal as well as inter-community variation by computing word-level measures for each time period of each community's history, INLINEFORM5 . Given a word INLINEFORM6 used within a particular community INLINEFORM7 at time INLINEFORM8 , we define two word-level measures:Specificity. We quantify the specificity INLINEFORM0 of INLINEFORM1 to INLINEFORM2 by calculating the PMI of INLINEFORM3 and INLINEFORM4 , relative to INLINEFORM5 , INLINEFORM6 where INLINEFORM0 is INLINEFORM1 's frequency in INLINEFORM2 . INLINEFORM3 is specific to INLINEFORM4 if it occurs more frequently in INLINEFORM5 than in the entire set INLINEFORM6 , hence distinguishing this community from the rest. A word INLINEFORM7 whose occurrence is decoupled from INLINEFORM8 , and thus has INLINEFORM9 close to 0, is said to be generic.We compute values of INLINEFORM0 for each time period INLINEFORM1 in INLINEFORM2 ; in the above description we drop the time-based subscripts for clarity.Volatility. We quantify the volatility INLINEFORM0 of INLINEFORM1 to INLINEFORM2 as the PMI of INLINEFORM3 and INLINEFORM4 relative to INLINEFORM5 , the entire history of INLINEFORM6 : INLINEFORM7 A word INLINEFORM0 is volatile at time INLINEFORM1 in INLINEFORM2 if it occurs more frequently at INLINEFORM3 than in the entire history INLINEFORM4 , behaving as a fad within a small window of time. A word that occurs with similar frequency across time, and hence has INLINEFORM5 close to 0, is said to be stable.Extending to utterances. Using our word-level primitives, we define the specificity of an utterance INLINEFORM0 in INLINEFORM1 , INLINEFORM2 as the average specificity of each word in the utterance. The volatility of utterances is defined analogously.Having described these word-level measures, we now proceed to establish the primary axes of our typology:Distinctiveness. A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 . We refer to a community with a less distinctive identity as being generic.Dynamicity. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . We refer to a community whose language is relatively consistent throughout time as being stable.In our subsequent analyses, we focus mostly on examing the average distinctiveness and dynamicity of a community over time, denoted INLINEFORM0 and INLINEFORM1 .We now explain how our typology can be applied to the particular setting of Reddit, and describe the overall behaviour of our linguistic axes in this context.Dataset description. Reddit is a popular website where users form and participate in discussion-based communities called subreddits. Within these communities, users post content—such as images, URLs, or questions—which often spark vibrant lengthy discussions in thread-based comment sections.The website contains many highly active subreddits with thousands of active subscribers. These communities span an extremely rich variety of topical interests, as represented by the examples described earlier. They also vary along a rich multitude of structural dimensions, such as the number of users, the amount of conversation and social interaction, and the social norms determining which types of content become popular. The diversity and scope of Reddit's multicommunity ecosystem make it an ideal landscape in which to closely examine the relation between varying community identities and social dynamics.Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 ).Estimating linguistic measures. We estimate word frequencies INLINEFORM0 , and by extension each downstream measure, in a carefully controlled manner in order to ensure we capture robust and meaningful linguistic behaviour. First, we only consider top-level comments which are initial responses to a post, as the content of lower-level responses might reflect conventions of dialogue more than a community's high-level interests. Next, in order to prevent a few highly active users from dominating our frequency estimates, we count each unique word once per user, ignoring successive uses of the same word by the same user. This ensures that our word-level characterizations are not skewed by a small subset of highly active contributors.In our subsequent analyses, we will only look at these measures computed over the nouns used in comments. In principle, our framework can be applied to any choice of vocabulary. However, in the case of Reddit using nouns provides a convenient degree of interpretability. We can easily understand the implication of a community preferentially mentioning a noun such as gamer or feminist, but interpreting the overuse of verbs or function words such as take or of is less straightforward. Additionally, in focusing on nouns we adopt the view emphasized in modern “third wave” accounts of sociolinguistic variation, that stylistic variation is inseparable from topical content BIBREF23 . In the case of online communities, the choice of what people choose to talk about serves as a primary signal of social identity. That said, a typology based on more purely stylistic differences is an interesting avenue for future work.Accounting for rare words. One complication when using measures such as PMI, which are based off of ratios of frequencies, is that estimates for very infrequent words could be overemphasized BIBREF24 . Words that only appear a few times in a community tend to score at the extreme ends of our measures (e.g. as highly specific or highly generic), obfuscating the impact of more frequent words in the community. To address this issue, we discard the long tail of infrequent words in our analyses, using only the top 5th percentile of words, by frequency within each INLINEFORM0 , to score comments and communities.Typology output on Reddit. The distribution of INLINEFORM0 and INLINEFORM1 across Reddit communities is shown in Figure FIGREF3 .B, along with examples of communities at the extremes of our typology. We find that interpretable groupings of communities emerge at various points within our axes. For instance, highly distinctive and dynamic communities tend to focus on rapidly-updating interests like sports teams and games, while generic and consistent communities tend to be large “link-sharing” hubs where users generally post content with no clear dominating themes. More examples of communities at the extremes of our typology are shown in Table TABREF9 .We note that these groupings capture abstract properties of a community's content that go beyond its topic. For instance, our typology relates topically contrasting communities such as yugioh (which is about a popular trading card game) and Seahawks through the shared trait that their content is particularly distinctive. Additionally, the axes can clarify differences between topically similar communities: while startrek and thewalkingdead both focus on TV shows, startrek is less dynamic than the median community, while thewalkingdead is among the most dynamic communities, as the show was still airing during the years considered.We have seen that our typology produces qualitatively satisfying groupings of communities according to the nature of their collective identity. This section shows that there is an informative and highly predictive relationship between a community's position in this typology and its user engagement patterns. We find that communities with distinctive and dynamic identities have higher rates of user engagement, and further show that a community's position in our identity-based landscape holds important predictive information that is complementary to a strong activity baseline.In particular user retention is one of the most crucial aspects of engagement and is critical to community maintenance BIBREF2 . We quantify how successful communities are at retaining users in terms of both short and long-term commitment. Our results indicate that rates of user retention vary drastically, yet systematically according to how distinctive and dynamic a community is (Figure FIGREF3 ).We find a strong, explanatory relationship between the temporal consistency of a community's identity and rates of user engagement: dynamic communities that continually update and renew their discussion content tend to have far higher rates of user engagement. The relationship between distinctiveness and engagement is less universal, but still highly informative: niche communities tend to engender strong, focused interest from users at one particular point in time, though this does not necessarily translate into long-term retention.We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right).Monthly retention is formally defined as the proportion of users who contribute in month INLINEFORM0 and then return to contribute again in month INLINEFORM1 . Each monthly datapoint is treated as unique and the trends in Figure FIGREF11 show 95% bootstrapped confidence intervals, cluster-resampled at the level of subreddit BIBREF25 , to account for differences in the number of months each subreddit contributes to the data.Importantly, we find that in the task of predicting community-level user retention our identity-based typology holds additional predictive value on top of strong baseline features based on community-size (# contributing users) and activity levels (mean # contributions per user), which are commonly used for churn prediction BIBREF7 . We compared out-of-sample predictive performance via leave-one-community-out cross validation using random forest regressors with ensembles of size 100, and otherwise default hyperparameters BIBREF26 . A model predicting average monthly retention based on a community's average distinctiveness and dynamicity achieves an average mean squared error ( INLINEFORM0 ) of INLINEFORM1 and INLINEFORM2 , while an analogous model predicting based on a community's size and average activity level (both log-transformed) achieves INLINEFORM4 and INLINEFORM5 . The difference between the two models is not statistically significant ( INLINEFORM6 , Wilcoxon signed-rank test). However, combining features from both models results in a large and statistically significant improvement over each independent model ( INLINEFORM7 , INLINEFORM8 , INLINEFORM9 Bonferroni-corrected pairwise Wilcoxon tests). These results indicate that our typology can explain variance in community-level retention rates, and provides information beyond what is present in standard activity-based features.As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content.To measure user tenures we focused on one slice of data (May, 2013) and measured how many months a user spends in each community, on average—the average number of months between a user's first and last comment in each community. We have activity data up until May 2015, so the maximum tenure is 24 months in this set-up, which is exceptionally long relative to the average community member (throughout our entire data less than INLINEFORM0 of users have tenures of more than 24 months in any community).The previous section shows that there is a strong connection between the nature of a community's identity and its basic user engagement patterns. In this section, we probe the relationship between a community's identity and how permeable, or accessible, it is to outsiders.We measure this phenomenon using what we call the acculturation gap, which compares the extent to which engaged vs. non-engaged users employ community-specific language. While previous work has found this gap to be large and predictive of future user engagement in two beer-review communities BIBREF5 , we find that the size of the acculturation gap depends strongly on the nature of a community's identity, with the gap being most pronounced in stable, highly distinctive communities (Figure FIGREF13 ).This finding has important implications for our understanding of online communities. Though many works have analyzed the dynamics of “linguistic belonging” in online communities BIBREF16 , BIBREF28 , BIBREF5 , BIBREF17 , our results suggest that the process of linguistically fitting in is highly contingent on the nature of a community's identity. At one extreme, in generic communities like pics or worldnews there is no distinctive, linguistic identity for users to adopt.To measure the acculturation gap for a community, we follow Danescu-Niculescu-Mizil et al danescu-niculescu-mizilno2013 and build “snapshot language models” (SLMs) for each community, which capture the linguistic state of a community at one point of time. Using these language models we can capture how linguistically close a particular utterance is to the community by measuring the cross-entropy of this utterance relative to the SLM: DISPLAYFORM0 where INLINEFORM0 is the probability assigned to bigram INLINEFORM1 from comment INLINEFORM2 in community-month INLINEFORM3 . We build the SLMs by randomly sampling 200 active users—defined as users with at least 5 comments in the respective community and month. For each of these 200 active users we select 5 random 10-word spans from 5 unique comments. To ensure robustness and maximize data efficiency, we construct 100 SLMs for each community-month pair that has enough data, bootstrap-resampling from the set of active users.We compute a basic measure of the acculturation gap for a community-month INLINEFORM0 as the relative difference of the cross-entropy of comments by users active in INLINEFORM1 with that of singleton comments by outsiders—i.e., users who only ever commented once in INLINEFORM2 , but who are still active in Reddit in general: DISPLAYFORM0 INLINEFORM0 denotes the distribution over singleton comments, INLINEFORM1 denotes the distribution over comments from users active in INLINEFORM2 , and INLINEFORM3 the expected values of the cross-entropy over these respective distributions. For each bootstrap-sampled SLM we compute the cross-entropy of 50 comments by active users (10 comments from 5 randomly sampled active users, who were not used to construct the SLM) and 50 comments from randomly-sampled outsiders.Figure FIGREF13 .A shows that the acculturation gap varies substantially with how distinctive and dynamic a community is. Highly distinctive communities have far higher acculturation gaps, while dynamicity exhibits a non-linear relationship: relatively stable communities have a higher linguistic `entry barrier', as do very dynamic ones. Thus, in communities like IAmA (a general Q&A forum) that are very generic, with content that is highly, but not extremely dynamic, outsiders are at no disadvantage in matching the community's language. In contrast, the acculturation gap is large in stable, distinctive communities like Cooking that have consistent community-specific language. The gap is also large in extremely dynamic communities like Seahawks, which perhaps require more attention or interest on the part of active users to keep up-to-date with recent trends in content.These results show that phenomena like the acculturation gap, which were previously observed in individual communities BIBREF28 , BIBREF5 , cannot be easily generalized to a larger, heterogeneous set of communities. At the same time, we see that structuring the space of possible communities enables us to observe systematic patterns in how such phenomena vary.Through the acculturation gap, we have shown that communities exhibit large yet systematic variations in their permeability to outsiders. We now turn to understanding the divide in commenting behaviour between outsiders and active community members at a finer granularity, by focusing on two particular ways in which such gaps might manifest among users: through different levels of engagement with specific content and with temporally volatile content.Echoing previous results, we find that community type mediates the extent and nature of the divide in content affinity. While in distinctive communities active members have a higher affinity for both community-specific content and for highly volatile content, the opposite is true for generic communities, where it is the outsiders who engage more with volatile content.We quantify these divides in content affinity by measuring differences in the language of the comments written by active users and outsiders. Concretely, for each community INLINEFORM0 , we define the specificity gap INLINEFORM1 as the relative difference between the average specificity of comments authored by active members, and by outsiders, where these measures are macroaveraged over users. Large, positive INLINEFORM2 then occur in communities where active users tend to engage with substantially more community-specific content than outsiders.We analogously define the volatility gap INLINEFORM0 as the relative difference in volatilities of active member and outsider comments. Large, positive values of INLINEFORM1 characterize communities where active users tend to have more volatile interests than outsiders, while negative values indicate communities where active users tend to have more stable interests.We find that in 94% of communities, INLINEFORM0 , indicating (somewhat unsurprisingly) that in almost all communities, active users tend to engage with more community-specific content than outsiders. However, the magnitude of this divide can vary greatly: for instance, in Homebrewing, which is dedicated to brewing beer, the divide is very pronounced ( INLINEFORM1 0.33) compared to funny, a large hub where users share humorous content ( INLINEFORM2 0.011).The nature of the volatility gap is comparatively more varied. In Homebrewing ( INLINEFORM0 0.16), as in 68% of communities, active users tend to write more volatile comments than outsiders ( INLINEFORM1 0). However, communities like funny ( INLINEFORM2 -0.16), where active users contribute relatively stable comments compared to outsiders ( INLINEFORM3 0), are also well-represented on Reddit.To understand whether these variations manifest systematically across communities, we examine the relationship between divides in content affinity and community type. In particular, following the intuition that active users have a relatively high affinity for a community's niche, we expect that the distinctiveness of a community will be a salient mediator of specificity and volatility gaps. Indeed, we find a strong correlation between a community's distinctiveness and its specificity gap (Spearman's INLINEFORM0 0.34, INLINEFORM1 0.001).We also find a strong correlation between distinctiveness and community volatility gaps (Spearman's INLINEFORM0 0.53, INLINEFORM1 0.001). In particular, we see that among the most distinctive communities (i.e., the top third of communities by distinctiveness), active users tend to write more volatile comments than outsiders (mean INLINEFORM2 0.098), while across the most generic communities (i.e., the bottom third), active users tend to write more stable comments (mean INLINEFORM3 -0.047, Mann-Whitney U test INLINEFORM4 0.001). The relative affinity of outsiders for volatile content in these communities indicates that temporally ephemeral content might serve as an entry point into such a community, without necessarily engaging users in the long term.Our language-based typology and analysis of user engagement draws on and contributes to several distinct research threads, in addition to the many foundational studies cited in the previous sections.Multicommunity studies. Our investigation of user engagement in multicommunity settings follows prior literature which has examined differences in user and community dynamics across various online groups, such as email listservs. Such studies have primarily related variations in user behaviour to structural features such as group size and volume of content BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 . In focusing on the linguistic content of communities, we extend this research by providing a content-based framework through which user engagement can be examined.Reddit has been a particularly useful setting for studying multiple communities in prior work. Such studies have mostly focused on characterizing how individual users engage across a multi-community platform BIBREF34 , BIBREF35 , or on specific user engagement patterns such as loyalty to particular communities BIBREF22 . We complement these studies by seeking to understand how features of communities can mediate a broad array of user engagement patterns within them.Typologies of online communities. Prior attempts to typologize online communities have primarily been qualitative and based on hand-designed categories, making them difficult to apply at scale. These typologies often hinge on having some well-defined function the community serves, such as supporting a business or non-profit cause BIBREF36 , which can be difficult or impossible to identify in massive, anonymous multi-community settings. Other typologies emphasize differences in communication platforms and other functional requirements BIBREF37 , BIBREF38 , which are important but preclude analyzing differences between communities within the same multi-community platform. Similarly, previous computational methods of characterizing multiple communities have relied on the presence of markers such as affixes in community names BIBREF35 , or platform-specific affordances such as evaluation mechanisms BIBREF39 .Our typology is also distinguished from community detection techniques that rely on structural or functional categorizations BIBREF40 , BIBREF41 . While the focus of those studies is to identify and characterize sub-communities within a larger social network, our typology provides a characterization of pre-defined communities based on the nature of their identity.Broader work on collective identity. Our focus on community identity dovetails with a long line of research on collective identity and user engagement, in both online and offline communities BIBREF42 , BIBREF1 , BIBREF2 . These studies focus on individual-level psychological manifestations of collective (or social) identity, and their relationship to user engagement BIBREF42 , BIBREF43 , BIBREF44 , BIBREF0 .In contrast, we seek to characterize community identities at an aggregate level and in an interpretable manner, with the goal of systematically organizing the diverse space of online communities. Typologies of this kind are critical to these broader, social-psychological studies of collective identity: they allow researchers to systematically analyze how the psychological manifestations and implications of collective identity vary across diverse sets of communities.Our current understanding of engagement patterns in online communities is patched up from glimpses offered by several disparate studies focusing on a few individual communities. This work calls into attention the need for a method to systematically reason about similarities and differences across communities. By proposing a way to structure the multi-community space, we find not only that radically contrasting engagement patterns emerge in different parts of this space, but also that this variation can be at least partly explained by the type of identity each community fosters.Our choice in this work is to structure the multi-community space according to a typology based on community identity, as reflected in language use. We show that this effectively explains cross-community variation of three different user engagement measures—retention, acculturation and content affinity—and complements measures based on activity and size with additional interpretable information. For example, we find that in niche communities established members are more likely to engage with volatile content than outsiders, while the opposite is true in generic communities. Such insights can be useful for community maintainers seeking to understand engagement patterns in their own communities.One main area of future research is to examine the temporal dynamics in the multi-community landscape. By averaging our measures of distinctiveness and dynamicity across time, our present study treated community identity as a static property. However, as communities experience internal changes and respond to external events, we can expect the nature of their identity to shift as well. For instance, the relative consistency of harrypotter may be disrupted by the release of a new novel, while Seahawks may foster different identities during and between football seasons. Conversely, a community's type may also mediate the impact of new events. Moving beyond a static view of community identity could enable us to better understand how temporal phenomena such as linguistic change manifest across different communities, and also provide a more nuanced view of user engagement—for instance, are communities more welcoming to newcomers at certain points in their lifecycle?Another important avenue of future work is to explore other ways of mapping the landscape of online communities. For example, combining structural properties of communities BIBREF40 with topical information BIBREF35 and with our identity-based measures could further characterize and explain variations in user engagement patterns. Furthermore, extending the present analyses to even more diverse communities supported by different platforms (e.g., GitHub, StackExchange, Wikipedia) could enable the characterization of more complex behavioral patterns such as collaboration and altruism, which become salient in different multicommunity landscapes.The authors thank Liye Fu, Jack Hessel, David Jurgens and Lillian Lee for their helpful comments. This research has been supported in part by a Discovery and Innovation Research Seed Award from the Office of the Vice Provost for Research at Cornell, NSF CNS-1010921, IIS-1149837, IIS-1514268 NIH BD2K, ARO MURI, DARPA XDATA, DARPA SIMPLEX, DARPA NGS2, Stanford Data Science Initiative, SAP Stanford Graduate Fellowship, NSERC PGS-D, Boeing, Lightspeed, and Volkswagen. | [
"Do they report results only on English data?",
"How do the various social phenomena examined manifest in different types of communities?",
"What patterns do they observe about how user engagement varies with the characteristics of a community?",
"How did the select the 300 Reddit communities for comparison?",
"How do the authors measure how temporally dynamic a community is?",
"How do the authors measure how distinctive a community is?"
] | [
[
"",
""
],
[
"Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community - a short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content.\n"
],
[
""
],
[
"They selected all the subreddits from January 2013 to December 2014 with at least 500 words in the vocabulary and at least 4 months of the subreddit's history. They also removed communities with the bulk of the contributions are in foreign language.",
"They collect subreddits from January 2013 to December 2014,2 for which there are at\nleast 500 words in the vocabulary used to estimate the measures,\nin at least 4 months of the subreddit’s history. They compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language."
],
[
""
],
[
""
]
] |
Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6.Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component.Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain.Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai“远端胃切除标本：小弯长11.5cm，大弯长17.0cm。距上切端6.0cm、下切端8.0cm" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai“上切缘距离"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data.In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word.For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model.Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai“远端胃切除" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively.There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.$Attention$ denotes the traditional attention and it can be defined as follows.where $d_k$ is the length of hidden vector.The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\left\langle l_s, 2\right\rangle $ where $l_s$ denotes the length of sequence.Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively.Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model.In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold.Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer.To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts.Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score.To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\times $ refers to removing that part from our model.As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model.There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance.To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way.In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset.We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for “Precision Medical Research" (No. 2018YFC0910500). | [
"What data is the language model pretrained on?",
"What baselines is the proposed model compared against?",
"How is the clinical text structuring task defined?",
"What are the specific tasks being unified?",
"Is all text in this dataset a question, or are there unrelated sentences in between questions?",
"How many questions are in the dataset?",
"What is the perWhat are the tasks evaluated?",
"Are there privacy concerns with clinical data?",
"How they introduce domain-specific features into pre-trained language model?",
"How big is QA-CTS task dataset?",
"How big is dataset of pathology reports collected from Ruijing Hospital?",
"What are strong baseline models in specific tasks?"
] | [
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"",
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],
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"",
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],
[
"",
"CTS is extracting structural data from medical research data (unstructured). Authors define QA-CTS task that aims to discover most related text from original text."
],
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"",
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],
[
"the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences "
],
[
"2,714 "
],
[
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],
[
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],
[
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Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 .Specifically focused on language modeling, this paper examines an issue that to our knowledge has not been explored: advances in neural language models have come at a significant cost in terms of increased computational complexity. Computing the probability of a token sequence using non-neural techniques requires a number of phrase lookups and perhaps a few arithmetic operations, whereas model inference with NLMs require large matrix multiplications consuming perhaps millions of floating point operations (FLOPs). These performance tradeoffs are worth discussing.In truth, language models exist in a quality–performance tradeoff space. As model quality increases (e.g., lower perplexity), performance as measured in terms of energy consumption, query latency, etc. tends to decrease. For applications primarily running in the cloud—say, machine translation—practitioners often solely optimize for the lowest perplexity. This is because such applications are embarrassingly parallel and hence trivial to scale in a data center environment.There are, however, applications of NLMs that require less one-sided optimizations. On mobile devices such as smartphones and tablets, for example, NLMs may be integrated into software keyboards for next-word prediction, allowing much faster text entry. Popular Android apps that enthusiastically tout this technology include SwiftKey and Swype. The greater computational costs of NLMs lead to higher energy usage in model inference, translating into shorter battery life.In this paper, we examine the quality–performance tradeoff in the shift from non-neural to neural language models. In particular, we compare Kneser–Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today. The decrease in perplexity on standard datasets has been well documented BIBREF3 , but to our knowledge no one has examined the performances tradeoffs. With deployment on a mobile device in mind, we evaluate energy usage and inference latency on a Raspberry Pi (which shares the same ARM architecture as nearly all smartphones today). We find that a 2.5 $\times $ reduction in perplexity on PTB comes at a staggering cost in terms of performance: inference with NLMs takes 49 $\times $ longer and requires 32 $\times $ more energy. Furthermore, we find that impressive reductions in perplexity translate into at best modest improvements in next-word prediction, which is arguable a better metric for evaluating software keyboards on a smartphone. The contribution of this paper is the first known elucidation of this quality–performance tradeoff. Note that we refrain from prescriptive recommendations: whether or not a tradeoff is worthwhile depends on the application. Nevertheless, NLP engineers should arguably keep these tradeoffs in mind when selecting a particular operating point. BIBREF3 evaluate recent neural language models; however, their focus is not on the computational footprint of each model, but rather the perplexity. To further reduce perplexity, many neural language model extensions exist, such as continuous cache pointer BIBREF5 and mixture of softmaxes BIBREF6 . Since our focus is on comparing “core” neural and non-neural approaches, we disregard these extra optimizations techniques in all of our models.Other work focus on designing lightweight models for resource-efficient inference on mobile devices. BIBREF7 explore LSTMs BIBREF8 with binary weights for language modeling; BIBREF9 examine shallow feedforward neural networks for natural language processing.AWD-LSTM. BIBREF4 show that a simple three-layer LSTM, with proper regularization and optimization techniques, can achieve state of the art on various language modeling datasets, surpassing more complex models. Specifically, BIBREF4 apply randomized backpropagation through time, variational dropout, activation regularization, embedding dropout, and temporal activation regularization. A novel scheduler for optimization, non-monotonically triggered ASGD (NT-ASGD) is also introduced. BIBREF4 name their three-layer LSTM model trained with such tricks, “AWD-LSTM.”Quasi-Recurrent Neural Networks. Quasi-recurrent neural networks (QRNNs; BIBREF10 ) achieve current state of the art in word-level language modeling BIBREF11 . A quasi-recurrent layer comprises two separate parts: a convolution layer with three weights, and a recurrent pooling layer. Given an input $\mathbf {X} \in \mathbb {R}^{k \times n}$ , the convolution layer is $
\mathbf {Z} = \tanh (\mathbf {W}_z \cdot \mathbf {X})\\
\mathbf {F} = \sigma (\mathbf {W}_f \cdot \mathbf {X})\\
\mathbf {O} = \sigma (\mathbf {W}_o \cdot \mathbf {X})
$ where $\sigma $ denotes the sigmoid function, $\cdot $ represents masked convolution across time, and $\mathbf {W}_{\lbrace z, f, o\rbrace } \in \mathbb {R}^{m \times k \times r}$ are convolution weights with $k$ input channels, $m$ output channels, and a window size of $r$ . In the recurrent pooling layer, the convolution outputs are combined sequentially: $
\mathbf {c}_t &= \mathbf {f}_t \odot \mathbf {c}_{t-1} + (1 -
\mathbf {f}_t) \odot \mathbf {z}_t\\
\mathbf {h}_t &= \mathbf {o}_t \odot \mathbf {c}_t
$ Multiple QRNN layers can be stacked for deeper hierarchical representation, with the output $\mathbf {h}_{1:t}$ being fed as the input into the subsequent layer: In language modeling, a four-layer QRNN is a standard architecture BIBREF11 .Perplexity–Recall Scale. Word-level perplexity does not have a strictly monotonic relationship with recall-at- $k$ , the fraction of top $k$ predictions that contain the correct word. A given R@ $k$ imposes a weak minimum perplexity constraint—there are many free parameters that allow for large variability in the perplexity given a certain R@ $k$ . Consider the corpus, “choo choo train,” with an associated unigram model $P(\text{``choo''}) = 0.1$ , $P(\text{``train''}) = 0.9$ , resulting in an R@1 of $1/3$ and perplexity of $4.8$ . Clearly, R@1 $ =1/3$ for all $P(\text{``choo''}) \le 0.5$ ; thus, perplexity can drop as low as 2 without affecting recall.We conducted our experiments on Penn Treebank (PTB; BIBREF12 ) and WikiText-103 (WT103; BIBREF13 ). Preprocessed by BIBREF14 , PTB contains 887K tokens for training, 70K for validation, and 78K for test, with a vocabulary size of 10,000. On the other hand, WT103 comprises 103 million tokens for training, 217K for validation, and 245K for test, spanning a vocabulary of 267K unique tokens.For the neural language model, we used a four-layer QRNN BIBREF10 , which achieves state-of-the-art results on a variety of datasets, such as WT103 BIBREF11 and PTB. To compare against more common LSTM architectures, we also evaluated AWD-LSTM BIBREF4 on PTB. For the non-neural approach, we used a standard five-gram model with modified Kneser-Ney smoothing BIBREF15 , as explored in BIBREF16 on PTB. We denote the QRNN models for PTB and WT103 as ptb-qrnn and wt103-qrnn, respectively.For each model, we examined word-level perplexity, R@3 in next-word prediction, latency (ms/q), and energy usage (mJ/q). To explore the perplexity–recall relationship, we collected individual perplexity and recall statistics for each sentence in the test set.The QRNN models followed the exact training procedure and architecture delineated in the official codebase from BIBREF11 . For ptb-qrnn, we trained the model for 550 epochs using NT-ASGD BIBREF4 , then finetuned for 300 epochs using ASGD BIBREF17 , all with a learning rate of 30 throughout. For wt103-qrnn, we followed BIBREF11 and trained the QRNN for 14 epochs, using the Adam optimizer with a learning rate of $10^{-3}$ . We also applied regularization techniques from BIBREF4 ; all the specific hyperparameters are the same as those in the repository. Our model architecture consists of 400-dimensional tied embedding weights BIBREF18 and four QRNN layers, with 1550 hidden units per layer on PTB and 2500 per layer on WT103. Both QRNN models have window sizes of $r=2$ for the first layer and $r=1$ for the rest.For the KN-5 model, we trained an off-the-shelf five-gram model using the popular SRILM toolkit BIBREF19 . We did not specify any special hyperparameters.We trained the QRNNs with PyTorch (0.4.0; commit 1807bac) on a Titan V GPU. To evaluate the models under a resource-constrained environment, we deployed them on a Raspberry Pi 3 (Model B) running Raspbian Stretch (4.9.41-v7+). The Raspberry Pi (RPi) is not only a standard platform, but also a close surrogate to mobile phones, using the same Cortex-A7 in many phones. We then transferred the trained models to the RPi, using the same frameworks for evaluation. We plugged the RPi into a Watts Up Pro meter, a power meter that can be read programatically over USB at a frequency of 1 Hz. For the QRNNs, we used the first 350 words of the test set, and averaged the ms/query and mJ/query. For KN-5, we used the entire test set for evaluation, since the latency was much lower. To adjust for the base power load, we subtracted idle power draw from energy usage.For a different perspective, we further evaluated all the models under a desktop environment, using an i7-4790k CPU and Titan V GPU. Because the base power load for powering a desktop is much higher than running neural language models, we collected only latency statistics. We used the entire test set, since the QRNN runs quickly.In addition to energy and latency, another consideration for the NLP developer selecting an operating point is the cost of underlying hardware. For our setup, the RPi costs $35 USD, the CPU costs $350 USD, and the GPU costs $3000 USD.To demonstrate the effectiveness of the QRNN models, we present the results of past and current state-of-the-art neural language models in Table 1 ; we report the Skip- and AWD-LSTM results as seen in the original papers, while we report our QRNN results. Skip LSTM denotes the four-layer Skip LSTM in BIBREF3 . BIBREF20 focus on Hebbian softmax, a model extension technique—Rae-LSTM refers to their base LSTM model without any extensions. In our results, KN-5 refers to the traditional five-gram model with modified Kneser-Ney smoothing, and AWD is shorthand for AWD-LSTM.Perplexity–recall scale. In Figure 1 , using KN-5 as the model, we plot the log perplexity (cross entropy) and R@3 error ( $1 - \text{R@3}$ ) for every sentence in PTB and WT103. The horizontal clusters arise from multiple perplexity points representing the same R@3 value, as explained in Section "Infrastructure" . We also observe that the perplexity–recall scale is non-linear—instead, log perplexity appears to have a moderate linear relationship with R@3 error on PTB ( $r=0.85$ ), and an even stronger relationship on WT103 ( $r=0.94$ ). This is partially explained by WT103 having much longer sentences, and thus less noisy statistics.From Figure 1 , we find that QRNN models yield strongly linear log perplexity–recall plots as well, where $r=0.88$ and $r=0.93$ for PTB and WT103, respectively. Note that, due to the improved model quality over KN-5, the point clouds are shifted downward compared to Figure 1 . We conclude that log perplexity, or cross entropy, provides a more human-understandable indicator of R@3 than perplexity does. Overall, these findings agree with those from BIBREF21 , which explores the log perplexity–word error rate scale in language modeling for speech recognition.Quality–performance tradeoff. In Table 2 , from left to right, we report perplexity results on the validation and test sets, R@3 on test, and finally per-query latency and energy usage. On the RPi, KN-5 is both fast and power-efficient to run, using only about 7 ms/query and 6 mJ/query for PTB (Table 2 , row 1), and 264 ms/q and 229 mJ/q on WT103 (row 5). Taking 220 ms/query and consuming 300 mJ/query, AWD-LSTM and ptb-qrnn are still viable for mobile phones: The modern smartphone holds upwards of 10,000 joules BIBREF22 , and the latency is within usability standards BIBREF23 . Nevertheless, the models are still 49 $\times $ slower and 32 $\times $ more power-hungry than KN-5. The wt103-qrnn model is completely unusable on phones, taking over 1.2 seconds per next-word prediction. Neural models achieve perplexity drops of 60–80% and R@3 increases of 22–34%, but these improvements come at a much higher cost in latency and energy usage.In Table 2 (last two columns), the desktop yields very different results: the neural models on PTB (rows 2–3) are 9 $\times $ slower than KN-5, but the absolute latency is only 8 ms/q, which is still much faster than what humans perceive as instantaneous BIBREF23 . If a high-end commodity GPU is available, then the models are only twice as slow as KN-5 is. From row 5, even better results are noted with wt103-qrnn: On the CPU, the QRNN is only 60% slower than KN-5 is, while the model is faster by 11 $\times $ on a GPU. These results suggest that, if only latency is considered under a commodity desktop environment, the QRNN model is humanly indistinguishable from the KN-5 model, even without using GPU acceleration.In the present work, we describe and examine the tradeoff space between quality and performance for the task of language modeling. Specifically, we explore the quality–performance tradeoffs between KN-5, a non-neural approach, and AWD-LSTM and QRNN, two neural language models. We find that with decreased perplexity comes vastly increased computational requirements: In one of the NLMs, a perplexity reduction by 2.5 $\times $ results in a 49 $\times $ rise in latency and 32 $\times $ increase in energy usage, when compared to KN-5. | [
"What aspects have been compared between various language models?",
"what classic language models are mentioned in the paper?",
"What is a commonly used evaluation metric for language models?"
] | [
[
"Quality measures using perplexity and recall, and performance measured using latency and energy usage. "
],
[
""
],
[
"",
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]
] |
Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-layer LSTM BIBREF1 ) to generate fake reviews, and concluded that these fake reviews look sufficiently genuine to fool native English speakers. They train their model using real restaurant reviews from yelp.com BIBREF2 . Once trained, the model is used to generate reviews character-by-character. Due to the generation methodology, it cannot be easily targeted for a specific context (meaningful side information). Consequently, the review generation process may stray off-topic. For instance, when generating a review for a Japanese restaurant in Las Vegas, the review generation process may include references to an Italian restaurant in Baltimore. The authors of BIBREF0 apply a post-processing step (customization), which replaces food-related words with more suitable ones (sampled from the targeted restaurant). The word replacement strategy has drawbacks: it can miss certain words and replace others independent of their surrounding words, which may alert savvy readers. As an example: when we applied the customization technique described in BIBREF0 to a review for a Japanese restaurant it changed the snippet garlic knots for breakfast with garlic knots for sushi).We propose a methodology based on neural machine translation (NMT) that improves the generation process by defining a context for the each generated fake review. Our context is a clear-text sequence of: the review rating, restaurant name, city, state and food tags (e.g. Japanese, Italian). We show that our technique generates review that stay on topic. We can instantiate our basic technique into several variants. We vet them on Amazon Mechanical Turk and find that native English speakers are very poor at recognizing our fake generated reviews. For one variant, the participants' performance is close to random: the class-averaged F-score of detection is INLINEFORM0 (whereas random would be INLINEFORM1 given the 1:6 imbalance in the test). Via a user study with experienced, highly educated participants, we compare this variant (which we will henceforth refer to as NMT-Fake* reviews) with fake reviews generated using the char-LSTM-based technique from BIBREF0 .We demonstrate that NMT-Fake* reviews constitute a new category of fake reviews that cannot be detected by classifiers trained only using previously known categories of fake reviews BIBREF0 , BIBREF3 , BIBREF4 . Therefore, NMT-Fake* reviews may go undetected in existing online review sites. To meet this challenge, we develop an effective classifier that detects NMT-Fake* reviews effectively (97% F-score). Our main contributions are:Fake reviews User-generated content BIBREF5 is an integral part of the contemporary user experience on the web. Sites like tripadvisor.com, yelp.com and Google Play use user-written reviews to provide rich information that helps other users choose where to spend money and time. User reviews are used for rating services or products, and for providing qualitative opinions. User reviews and ratings may be used to rank services in recommendations. Ratings have an affect on the outwards appearance. Already 8 years ago, researchers estimated that a one-star rating increase affects the business revenue by 5 – 9% on yelp.com BIBREF6 .Due to monetary impact of user-generated content, some businesses have relied on so-called crowd-turfing agents BIBREF7 that promise to deliver positive ratings written by workers to a customer in exchange for a monetary compensation. Crowd-turfing ethics are complicated. For example, Amazon community guidelines prohibit buying content relating to promotions, but the act of writing fabricated content is not considered illegal, nor is matching workers to customers BIBREF8 . Year 2015, approximately 20% of online reviews on yelp.com were suspected of being fake BIBREF9 .Nowadays, user-generated review sites like yelp.com use filters and fraudulent review detection techniques. These factors have resulted in an increase in the requirements of crowd-turfed reviews provided to review sites, which in turn has led to an increase in the cost of high-quality review. Due to the cost increase, researchers hypothesize the existence of neural network-generated fake reviews. These neural-network-based fake reviews are statistically different from human-written fake reviews, and are not caught by classifiers trained on these BIBREF0 .Detecting fake reviews can either be done on an individual level or as a system-wide detection tool (i.e. regulation). Detecting fake online content on a personal level requires knowledge and skills in critical reading. In 2017, the National Literacy Trust assessed that young people in the UK do not have the skillset to differentiate fake news from real news BIBREF10 . For example, 20% of children that use online news sites in age group 12-15 believe that all information on news sites are true.Neural Networks Neural networks are function compositions that map input data through INLINEFORM0 subsequent layers: DISPLAYFORM0 where the functions INLINEFORM0 are typically non-linear and chosen by experts partly for known good performance on datasets and partly for simplicity of computational evaluation. Language models (LMs) BIBREF11 are generative probability distributions that assign probabilities to sequences of tokens ( INLINEFORM1 ): DISPLAYFORM0 such that the language model can be used to predict how likely a specific token at time step INLINEFORM0 is, based on the INLINEFORM1 previous tokens. Tokens are typically either words or characters.For decades, deep neural networks were thought to be computationally too difficult to train. However, advances in optimization, hardware and the availability of frameworks have shown otherwise BIBREF1 , BIBREF12 . Neural language models (NLMs) have been one of the promising application areas. NLMs are typically various forms of recurrent neural networks (RNNs), which pass through the data sequentially and maintain a memory representation of the past tokens with a hidden context vector. There are many RNN architectures that focus on different ways of updating and maintaining context vectors: Long Short-Term Memory units (LSTM) and Gated Recurrent Units (GRUs) are perhaps most popular. Neural LMs have been used for free-form text generation. In certain application areas, the quality has been high enough to sometimes fool human readers BIBREF0 . Encoder-decoder (seq2seq) models BIBREF13 are architectures of stacked RNNs, which have the ability to generate output sequences based on input sequences. The encoder network reads in a sequence of tokens, and passes it to a decoder network (a LM). In contrast to simpler NLMs, encoder-decoder networks have the ability to use additional context for generating text, which enables more accurate generation of text. Encoder-decoder models are integral in Neural Machine Translation (NMT) BIBREF14 , where the task is to translate a source text from one language to another language. NMT models additionally use beam search strategies to heuristically search the set of possible translations. Training datasets are parallel corpora; large sets of paired sentences in the source and target languages. The application of NMT techniques for online machine translation has significantly improved the quality of translations, bringing it closer to human performance BIBREF15 .Neural machine translation models are efficient at mapping one expression to another (one-to-one mapping). Researchers have evaluated these models for conversation generation BIBREF16 , with mixed results. Some researchers attribute poor performance to the use of the negative log likelihood cost function during training, which emphasizes generation of high-confidence phrases rather than diverse phrases BIBREF17 . The results are often generic text, which lacks variation. Li et al. have suggested various augmentations to this, among others suppressing typical responses in the decoder language model to promote response diversity BIBREF17 .We discuss the attack model, our generative machine learning method and controlling the generative process in this section.Wang et al. BIBREF7 described a model of crowd-turfing attacks consisting of three entities: customers who desire to have fake reviews for a particular target (e.g. their restaurant) on a particular platform (e.g. Yelp), agents who offer fake review services to customers, and workers who are orchestrated by the agent to compose and post fake reviews.Automated crowd-turfing attacks (ACA) replace workers by a generative model. This has several benefits including better economy and scalability (human workers are more expensive and slower) and reduced detectability (agent can better control the rate at which fake reviews are generated and posted).We assume that the agent has access to public reviews on the review platform, by which it can train its generative model. We also assume that it is easy for the agent to create a large number of accounts on the review platform so that account-based detection or rate-limiting techniques are ineffective against fake reviews.The quality of the generative model plays a crucial role in the attack. Yao et al. BIBREF0 propose the use of a character-based LSTM as base for generative model. LSTMs are not conditioned to generate reviews for a specific target BIBREF1 , and may mix-up concepts from different contexts during free-form generation. Mixing contextually separate words is one of the key criteria that humans use to identify fake reviews. These may result in violations of known indicators for fake content BIBREF18 . For example, the review content may not match prior expectations nor the information need that the reader has. We improve the attack model by considering a more capable generative model that produces more appropriate reviews: a neural machine translation (NMT) model.We propose the use of NMT models for fake review generation. The method has several benefits: 1) the ability to learn how to associate context (keywords) to reviews, 2) fast training time, and 3) a high-degree of customization during production time, e.g. introduction of specific waiter or food items names into reviews.NMT models are constructions of stacked recurrent neural networks (RNNs). They include an encoder network and a decoder network, which are jointly optimized to produce a translation of one sequence to another. The encoder rolls over the input data in sequence and produces one INLINEFORM0 -dimensional context vector representation for the sentence. The decoder then generates output sequences based on the embedding vector and an attention module, which is taught to associate output words with certain input words. The generation typically continues until a specific EOS (end of sentence) token is encountered. The review length can be controlled in many ways, e.g. by setting the probability of generating the EOS token to zero until the required length is reached.NMT models often also include a beam search BIBREF14 , which generates several hypotheses and chooses the best ones amongst them. In our work, we use the greedy beam search technique. We forgo the use of additional beam searches as we found that the quality of the output was already adequate and the translation phase time consumption increases linearly for each beam used.We use the Yelp Challenge dataset BIBREF2 for our fake review generation. The dataset (Aug 2017) contains 2.9 million 1 –5 star restaurant reviews. We treat all reviews as genuine human-written reviews for the purpose of this work, since wide-scale deployment of machine-generated review attacks are not yet reported (Sep 2017) BIBREF19 . As preprocessing, we remove non-printable (non-ASCII) characters and excessive white-space. We separate punctuation from words. We reserve 15,000 reviews for validation and 3,000 for testing, and the rest we use for training. NMT models require a parallel corpus of source and target sentences, i.e. a large set of (source, target)-pairs. We set up a parallel corpus by constructing (context, review)-pairs from the dataset. Next, we describe how we created our input context.The Yelp Challenge dataset includes metadata about restaurants, including their names, food tags, cities and states these restaurants are located in. For each restaurant review, we fetch this metadata and use it as our input context in the NMT model. The corresponding restaurant review is similarly set as the target sentence. This method produced 2.9 million pairs of sentences in our parallel corpus. We show one example of the parallel training corpus in Example 1 below:5 Public House Las Vegas NV Gastropubs Restaurants > Excellentfood and service . Pricey , but well worth it . I would recommendthe bone marrow and sampler platter for appetizers . \end{verbatim} \noindent The order {\textbf{[rating name city state tags]}} is kept constant.Training the model conditions it to associate certain sequences of words in the input sentence with others in the output. \subsubsection{Training Settings} We train our NMT model on a commodity PC with a i7-4790k CPU (4.00GHz), with 32GB RAM and one NVidia GeForce GTX 980 GPU. Our system can process approximately 1,300 \textendash 1,500 source tokens/s and approximately 5,730 \textendash 5,830 output tokens/s. Training one epoch takes in average 72 minutes. The model is trained for 8 epochs, i.e. over night. We call fake review generated by this model \emph{NMT-Fake reviews}. We only need to train one model to produce reviews of different ratings.We use the training settings: adam optimizer \cite{kingma2014adam} with the suggested learning rate 0.001 \cite{klein2017opennmt}. For most parts, parameters are at their default values. Notably, the maximum sentence length of input and output is 50 tokens by default.We leverage the framework openNMT-py \cite{klein2017opennmt} to teach the our NMT model.We list used openNMT-py commands in Appendix Table~\ref{table:openNMT-py_commands}. \begin{figure}[t]\begin{center} \begin{tabular}{ | l | } \hlineExample 2. Greedy NMT \\Great food, \underline{great} service, \underline{great} \textit{\textit{beer selection}}. I had the \textit{Gastropubs burger} and it\\was delicious. The \underline{\textit{beer selection}} was also \underline{great}. \\\\Example 3. NMT-Fake* \\I love this restaurant. Great food, great service. It's \textit{a little pricy} but worth\\it for the \textit{quality} of the \textit{beer} and atmosphere you can see in \textit{Vegas}\\ \hline \end{tabular} \label{table:output_comparison}\end{center}\caption{Na\"{i}ve text generation with NMT vs. generation using our NTM model. Repetitive patterns are \underline{underlined}. Contextual words are \emph{italicized}. Both examples here are generated based on the context given in Example~1.}\label{fig:comparison}\end{figure} \subsection{Controlling generation of fake reviews}\label{sec:generating} Greedy NMT beam searches are practical in many NMT cases. However, the results are simply repetitive, when naively applied to fake review generation (See Example~2 in Figure~\ref{fig:comparison}).The NMT model produces many \emph{high-confidence} word predictions, which are repetitive and obviously fake. We calculated that in fact, 43\% of the generated sentences started with the phrase ``Great food''. The lack of diversity in greedy use of NMTs for text generation is clear. \begin{algorithm}[!b] \KwData{Desired review context $C_\mathrm{input}$ (given as cleartext), NMT model} \KwResult{Generated review $out$ for input context $C_\mathrm{input}$}set $b=0.3$, $\lambda=-5$, $\alpha=\frac{2}{3}$, $p_\mathrm{typo}$, $p_\mathrm{spell}$ \\$\log p \leftarrow \text{NMT.decode(NMT.encode(}C_\mathrm{input}\text{))}$ \\out $\leftarrow$ [~] \\$i \leftarrow 0$ \\$\log p \leftarrow \text{Augment}(\log p$, $b$, $\lambda$, $1$, $[~]$, 0)~~~~~~~~~~~~~~~ |~random penalty~\\\While{$i=0$ or $o_i$ not EOS}{$\log \Tilde{p} \leftarrow \text{Augment}(\log p$, $b$, $\lambda$, $\alpha$, $o_i$, $i$)~~~~~~~~~~~ |~start \& memory penalty~\\$o_i \leftarrow$ \text{NMT.beam}($\log \Tilde{p}$, out) \\out.append($o_i$) \\$i \leftarrow i+1$}\text{return}~$\text{Obfuscate}$(out,~$p_\mathrm{typo}$,~$p_\mathrm{spell}$)\caption{Generation of NMT-Fake* reviews.}\label{alg:base}\end{algorithm} In this work, we describe how we succeeded in creating more diverse and less repetitive generated reviews, such as Example 3 in Figure~\ref{fig:comparison}.We outline pseudocode for our methodology of generating fake reviews in Algorithm~\ref{alg:base}. There are several parameters in our algorithm.The details of the algorithm will be shown later.We modify the openNMT-py translation phase by changing log-probabilities before passing them to the beam search.We notice that reviews generated with openNMT-py contain almost no language errors. As an optional post-processing step, we obfuscate reviews by introducing natural typos/misspellings randomly. In the next sections, we describe how we succeeded in generating more natural sentences from our NMT model, i.e. generating reviews like Example~3 instead of reviews like Example~2. \subsubsection{Variation in word content} Example 2 in Figure~\ref{fig:comparison} repeats commonly occurring words given for a specific context (e.g. \textit{great, food, service, beer, selection, burger} for Example~1). Generic review generation can be avoided by decreasing probabilities (log-likelihoods \cite{murphy2012machine}) of the generators LM, the decoder.We constrain the generation of sentences by randomly \emph{imposing penalties to words}.We tried several forms of added randomness, and found that adding constant penalties to a \emph{random subset} of the target words resulted in the most natural sentence flow. We call these penalties \emph{Bernoulli penalties}, since the random variables are chosen as either 1 or 0 (on or off). \paragraph{Bernoulli penalties to language model}To avoid generic sentences components, we augment the default language model $p(\cdot)$ of the decoder by \begin{equation}\log \Tilde{p}(t_k) = \log p(t_k | t_i, \dots, t_1) + \lambda q,\end{equation} where $q \in R^{V}$ is a vector of Bernoulli-distributed random values that obtain values $1$ with probability $b$ and value $0$ with probability $1-b_i$, and $\lambda < 0$. Parameter $b$ controls how much of the vocabulary is forgotten and $\lambda$ is a soft penalty of including ``forgotten'' words in a review.$\lambda q_k$ emphasizes sentence forming with non-penalized words. The randomness is reset at the start of generating a new review.Using Bernoulli penalties in the language model, we can ``forget'' a certain proportion of words and essentially ``force'' the creation of less typical sentences. We will test the effect of these two parameters, the Bernoulli probability $b$ and log-likelihood penalty of including ``forgotten'' words $\lambda$, with a user study in Section~\ref{sec:varying}. \paragraph{Start penalty}We introduce start penalties to avoid generic sentence starts (e.g. ``Great food, great service''). Inspired by \cite{li2016diversity}, we add a random start penalty $\lambda s^\mathrm{i}$, to our language model, which decreases monotonically for each generated token. We set $\alpha \leftarrow 0.66$ as it's effect decreases by 90\% every 5 words generated. \paragraph{Penalty for reusing words}Bernoulli penalties do not prevent excessive use of certain words in a sentence (such as \textit{great} in Example~2).To avoid excessive reuse of words, we included a memory penalty for previously used words in each translation.Concretely, we add the penalty $\lambda$ to each word that has been generated by the greedy search. \subsubsection{Improving sentence coherence}\label{sec:grammar}We visually analyzed reviews after applying these penalties to our NMT model. While the models were clearly diverse, they were \emph{incoherent}: the introduction of random penalties had degraded the grammaticality of the sentences. Amongst others, the use of punctuation was erratic, and pronouns were used semantically wrongly (e.g. \emph{he}, \emph{she} might be replaced, as could ``and''/``but''). To improve the authenticity of our reviews, we added several \emph{grammar-based rules}. English language has several classes of words which are important for the natural flow of sentences.We built a list of common pronouns (e.g. I, them, our), conjunctions (e.g. and, thus, if), punctuation (e.g. ,/.,..), and apply only half memory penalties for these words. We found that this change made the reviews more coherent. The pseudocode for this and the previous step is shown in Algorithm~\ref{alg:aug}.The combined effect of grammar-based rules and LM augmentation is visible in Example~3, Figure~\ref{fig:comparison}. \begin{algorithm}[!t] \KwData{Initial log LM $\log p$, Bernoulli probability $b$, soft-penalty $\lambda$, monotonic factor $\alpha$, last generated token $o_i$, grammar rules set $G$} \KwResult{Augmented log LM $\log \Tilde{p}$}\begin{algorithmic}[1]\Procedure {Augment}{$\log p$, $b$, $\lambda$, $\alpha$, $o_i$, $i$}{ \\generate $P_{\mathrm{1:N}} \leftarrow Bernoulli(b)$~~~~~~~~~~~~~~~|~$\text{One value} \in \{0,1\}~\text{per token}$~ \\$I \leftarrow P>0$ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~|~Select positive indices~\\$\log \Tilde{p} \leftarrow$ $\text{Discount}$($\log p$, $I$, $\lambda \cdot \alpha^i$,$G$) ~~~~~~ |~start penalty~\\$\log \Tilde{p} \leftarrow$ $\text{Discount}$($\log \Tilde{p}$, $[o_i]$, $\lambda$,$G$) ~~~~~~~~~ |~memory penalty~\\\textbf{return}~$\log \Tilde{p}$}\EndProcedure\\\Procedure {Discount}{$\log p$, $I$, $\lambda$, $G$}{\State{\For{$i \in I$}{\eIf{$o_i \in G$}{$\log p_{i} \leftarrow \log p_{i} + \lambda/2$}{$\log p_{i} \leftarrow \log p_{i} + \lambda$}}\textbf{return}~$\log p$\EndProcedure}}\end{algorithmic}\caption{Pseudocode for augmenting language model. }\label{alg:aug}\end{algorithm} \subsubsection{Human-like errors}\label{sec:obfuscation}We notice that our NMT model produces reviews without grammar mistakes.This is unlike real human writers, whose sentences contain two types of language mistakes 1) \emph{typos} that are caused by mistakes in the human motoric input, and 2) \emph{common spelling mistakes}.We scraped a list of common English language spelling mistakes from Oxford dictionary\footnote{\url{https://en.oxforddictionaries.com/spelling/common-misspellings}} and created 80 rules for randomly \emph{re-introducing spelling mistakes}.Similarly, typos are randomly reintroduced based on the weighted edit distance\footnote{\url{https://pypi.python.org/pypi/weighted-levenshtein/0.1}}, such that typos resulting in real English words with small perturbations are emphasized.We use autocorrection tools\footnote{\url{https://pypi.python.org/pypi/autocorrect/0.1.0}} for finding these words.We call these augmentations \emph{obfuscations}, since they aim to confound the reader to think a human has written them. We omit the pseudocode description for brevity. \subsection{Experiment: Varying generation parameters in our NMT model}\label{sec:varying} Parameters $b$ and $\lambda$ control different aspects in fake reviews.We show six different examples of generated fake reviews in Table~\ref{table:categories}.Here, the largest differences occur with increasing values of $b$: visibly, the restaurant reviews become more extreme.This occurs because a large portion of vocabulary is ``forgotten''. Reviews with $b \geq 0.7$ contain more rare word combinations, e.g. ``!!!!!'' as punctuation, and they occasionally break grammaticality (''experience was awesome'').Reviews with lower $b$ are more generic: they contain safe word combinations like ``Great place, good service'' that occur in many reviews. Parameter $\lambda$'s is more subtle: it affects how random review starts are and to a degree, the discontinuation between statements within the review.We conducted an Amazon Mechanical Turk (MTurk) survey in order to determine what kind of NMT-Fake reviews are convincing to native English speakers. We describe the survey and results in the next section. \begin{table}[!b]\caption{Six different parametrizations of our NMT reviews and one example for each. The context is ``5 P~.~F~.~Chang ' s Scottsdale AZ'' in all examples.}\begin{center} \begin{tabular}{ | l | l | } \hline $(b, \lambda)$ & Example review for context \\ \hline \hline $(0.3, -3)$ & I love this location! Great service, great food and the best drinks in Scottsdale. \\ & The staff is very friendly and always remembers u when we come in\\\hline $(0.3, -5)$ & Love love the food here! I always go for lunch. They have a great menu and \\ & they make it fresh to order. Great place, good service and nice staff\\\hline $(0.5, -4)$ & I love their chicken lettuce wraps and fried rice!! The service is good, they are\\ & always so polite. They have great happy hour specials and they have a lot\\ & of options.\\\hline $(0.7, -3)$ & Great place to go with friends! They always make sure your dining \\ & experience was awesome.\\ \hline $(0.7, -5)$ & Still haven't ordered an entree before but today we tried them once..\\ & both of us love this restaurant....\\\hline $(0.9, -4)$ & AMAZING!!!!! Food was awesome with excellent service. Loved the lettuce \\ & wraps. Great drinks and wine! Can't wait to go back so soon!!\\ \hline \end{tabular} \label{table:categories}\end{center}\end{table} \subsubsection{MTurk study}\label{sec:amt}We created 20 jobs, each with 100 questions, and requested master workers in MTurk to complete the jobs.We randomly generated each survey for the participants. Each review had a 50\% chance to be real or fake. The fake ones further were chosen among six (6) categories of fake reviews (Table~\ref{table:categories}).The restaurant and the city was given as contextual information to the participants. Our aim was to use this survey to understand how well English-speakers react to different parametrizations of NMT-Fake reviews.Table~\ref{table:amt_pop} in Appendix summarizes the statistics for respondents in the survey. All participants were native English speakers from America. The base rate (50\%) was revealed to the participants prior to the study. We first investigated overall detection of any NMT-Fake reviews (1,006 fake reviews and 994 real reviews). We found that the participants had big difficulties in detecting our fake reviews. In average, the reviews were detected with class-averaged \emph{F-score of only 56\%}, with 53\% F-score for fake review detection and 59\% F-score for real review detection. The results are very close to \emph{random detection}, where precision, recall and F-score would each be 50\%. Results are recorded in Table~\ref{table:MTurk_super}. Overall, the fake review generation is very successful, since human detection rate across categories is close to random. \begin{table}[t]\caption{Effectiveness of Mechanical Turkers in distinguishing human-written reviews from fake reviews generated by our NMT model (all variants).}\begin{center} \begin{tabular}{ | c | c |c |c | c | } \hline \multicolumn{5}{|c|}{Classification report} \\ \hline Review Type & Precision & Recall & F-score & Support \\ \hline \hline Human & 55\% & 63\% & 59\% & 994\\ NMT-Fake & 57\% & 50\% & 53\% & 1006 \\ \hline \end{tabular} \label{table:MTurk_super}\end{center}\end{table} We noticed some variation in the detection of different fake review categories. The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \lambda=-5)$, where true positive rate was $40.4\%$, while the true negative rate of the real class was $62.7\%$. The precision were $16\%$ and $86\%$, respectively. The class-averaged F-score is $47.6\%$, which is close to random. Detailed classification reports are shown in Table~\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \emph{our NMT-Fake reviews pose a significant threat to review systems}, since \emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper. \section{Evaluation}\graphicspath{ {figures/}} We evaluate our fake reviews by first comparing them statistically to previously proposed types of fake reviews, and proceed with a user study with experienced participants. We demonstrate the statistical difference to existing fake review types \cite{yao2017automated,mukherjee2013yelp,rayana2015collective} by training classifiers to detect previous types and investigate classification performance. \subsection{Replication of state-of-the-art model: LSTM}\label{sec:repl} Yao et al. \cite{yao2017automated} presented the current state-of-the-art generative model for fake reviews. The model is trained over the Yelp Challenge dataset using a two-layer character-based LSTM model.We requested the authors of \cite{yao2017automated} for access to their LSTM model or a fake review dataset generated by their model. Unfortunately they were not able to share either of these with us. We therefore replicated their model as closely as we could, based on their paper and e-mail correspondence\footnote{We are committed to sharing our code with bonafide researchers for the sake of reproducibility.}. We used the same graphics card (GeForce GTX) and trained using the same framework (torch-RNN in lua). We downloaded the reviews from Yelp Challenge and preprocessed the data to only contain printable ASCII characters, and filtered out non-restaurant reviews. We trained the model for approximately 72 hours. We post-processed the reviews using the customization methodology described in \cite{yao2017automated} and email correspondence. We call fake reviews generated by this model LSTM-Fake reviews. \subsection{Similarity to existing fake reviews}\label{sec:automated} We now want to understand how NMT-Fake* reviews compare to a) LSTM fake reviews and b) human-generated fake reviews. We do this by comparing the statistical similarity between these classes. For `a' (Figure~\ref{fig:lstm}), we use the Yelp Challenge dataset. We trained a classifier using 5,000 random reviews from the Yelp Challenge dataset (``human'') and 5,000 fake reviews generated by LSTM-Fake. Yao et al. \cite{yao2017automated} found that character features are essential in identifying LSTM-Fake reviews. Consequently, we use character features (n-grams up to 3). For `b' (Figure~\ref{fig:shill}),we the ``Yelp Shills'' dataset (combination of YelpZip \cite{mukherjee2013yelp}, YelpNYC \cite{mukherjee2013yelp}, YelpChi \cite{rayana2015collective}). This dataset labels entries that are identified as fraudulent by Yelp's filtering mechanism (''shill reviews'')\footnote{Note that shill reviews are probably generated by human shills \cite{zhao2017news}.}. The rest are treated as genuine reviews from human users (''genuine''). We use 100,000 reviews from each category to train a classifier. We use features from the commercial psychometric tool LIWC2015 \cite{pennebaker2015development} to generated features. In both cases, we use AdaBoost (with 200 shallow decision trees) for training. For testing each classifier, we use a held out test set of 1,000 reviews from both classes in each case. In addition, we test 1,000 NMT-Fake* reviews. Figures~\ref{fig:lstm} and~\ref{fig:shill} show the results. The classification threshold of 50\% is marked with a dashed line. \begin{figure} \begin{subfigure}[b]{0.5\columnwidth} \includegraphics[width=\columnwidth]{figures/lstm.png} \caption{Human--LSTM reviews.} \label{fig:lstm} \end{subfigure} \begin{subfigure}[b]{0.5\columnwidth} \includegraphics[width=\columnwidth]{figures/distribution_shill.png} \caption{Genuine--Shill reviews.} \label{fig:shill} \end{subfigure} \caption{ Histogram comparison of NMT-Fake* reviews with LSTM-Fake reviews and human-generated (\emph{genuine} and \emph{shill}) reviews. Figure~\ref{fig:lstm} shows that a classifier trained to distinguish ``human'' vs. LSTM-Fake cannot distinguish ``human'' vs NMT-Fake* reviews. Figure~\ref{fig:shill} shows NMT-Fake* reviews are more similar to \emph{genuine} reviews than \emph{shill} reviews. } \label{fig:statistical_similarity}\end{figure} We can see that our new generated reviews do not share strong attributes with previous known categories of fake reviews. If anything, our fake reviews are more similar to genuine reviews than previous fake reviews. We thus conjecture that our NMT-Fake* fake reviews present a category of fake reviews that may go undetected on online review sites. \subsection{Comparative user study}\label{sec:comparison}We wanted to evaluate the effectiveness of fake reviews againsttech-savvy users who understand and know to expect machine-generated fake reviews. We conducted a user study with 20 participants, all with computer science education and at least one university degree. Participant demographics are shown in Table~\ref{table:amt_pop} in the Appendix. Each participant first attended a training session where they were asked to label reviews (fake and genuine) and could later compare them to the correct answers -- we call these participants \emph{experienced participants}.No personal data was collected during the user study. Each person was given two randomly selected sets of 30 of reviews (a total of 60 reviews per person) with reviews containing 10 \textendash 50 words each.Each set contained 26 (87\%) real reviews from Yelp and 4 (13\%) machine-generated reviews,numbers chosen based on suspicious review prevalence on Yelp~\cite{mukherjee2013yelp,rayana2015collective}.One set contained machine-generated reviews from one of the two models (NMT ($b=0.3, \lambda=-5$) or LSTM),and the other set reviews from the other in randomized order. The number of fake reviews was revealed to each participant in the study description. Each participant was requested to mark four (4) reviews as fake. Each review targeted a real restaurant. A screenshot of that restaurant's Yelp page was shown to each participant prior to the study. Each participant evaluated reviews for one specific, randomly selected, restaurant. An example of the first page of the user study is shown in Figure~\ref{fig:screenshot} in Appendix. \begin{figure}[!ht]\centering\includegraphics[width=.7\columnwidth]{detection2.png}\caption{Violin plots of detection rate in comparative study. Mean and standard deviations for number of detected fakes are $0.8\pm0.7$ for NMT-Fake* and $2.5\pm1.0$ for LSTM-Fake. $n=20$. A sample of random detection is shown as comparison.}\label{fig:aalto}\end{figure} Figure~\ref{fig:aalto} shows the distribution of detected reviews of both types. A hypothetical random detector is shown for comparison.NMT-Fake* reviews are significantly more difficult to detect for our experienced participants. In average, detection rate (recall) is $20\%$ for NMT-Fake* reviews, compared to $61\%$ for LSTM-based reviews.The precision (and F-score) is the same as the recall in our study, since participants labeled 4 fakes in each set of 30 reviews \cite{murphy2012machine}.The distribution of the detection across participants is shown in Figure~\ref{fig:aalto}. \emph{The difference is statistically significant with confidence level $99\%$} (Welch's t-test).We compared the detection rate of NMT-Fake* reviews to a random detector, and find that \emph{our participants detection rate of NMT-Fake* reviews is not statistically different from random predictions with 95\% confidence level} (Welch's t-test). \section{Defenses} \label{sec:detection} We developed an AdaBoost-based classifier to detect our new fake reviews, consisting of 200 shallow decision trees (depth 2). The features we used are recorded in Table~\ref{table:features_adaboost} (Appendix).We used word-level features based on spaCy-tokenization \cite{honnibal-johnson:2015:EMNLP} and constructed n-gram representation of POS-tags and dependency tree tags. We added readability features from NLTK~\cite{bird2004nltk}. \begin{figure}[ht]\centering\includegraphics[width=.7\columnwidth]{obf_score_fair_2.png}\caption{Adaboost-based classification of NMT-Fake and human-written reviews.Effect of varying $b$ and $\lambda$ in fake review generation.The variant native speakers had most difficulties detecting is well detectable by AdaBoost (97\%).}\label{fig:adaboost_matrix_b_lambda}\end{figure} Figure~\ref{fig:adaboost_matrix_b_lambda} shows our AdaBoost classifier's class-averaged F-score at detecting different kind of fake reviews. The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \lambda=-5$) are detected with an excellent 97\% F-score.The most important features for the classification were counts for frequently occurring words in fake reviews (such as punctuation, pronouns, articles) as well as the readability feature ``Automated Readability Index''. We thus conclude that while NMT-Fake reviews are difficult to detect for humans, they can be well detected with the right tools. \section{Related Work} Kumar and Shah~\cite{kumar2018false} survey and categorize false information research. Automatically generated fake reviews are a form of \emph{opinion-based false information}, where the creator of the review may influence reader's opinions or decisions.Yao et al. \cite{yao2017automated} presented their study on machine-generated fake reviews. Contrary to us, they investigated character-level language models, without specifying a specific context before generation. We leverage existing NMT tools to encode a specific context to the restaurant before generating reviews.Supporting our study, Everett et al~\cite{Everett2016Automated} found that security researchers were less likely to be fooled by Markov chain-generated Reddit comments compared to ordinary Internet users. Diversification of NMT model outputs has been studied in \cite{li2016diversity}. The authors proposed the use of a penalty to commonly occurring sentences (\emph{n-grams}) in order to emphasize maximum mutual information-based generation.The authors investigated the use of NMT models in chatbot systems.We found that unigram penalties to random tokens (Algorithm~\ref{alg:aug}) was easy to implement and produced sufficiently diverse responses. \section {Discussion and Future Work} \paragraph{What makes NMT-Fake* reviews difficult to detect?} First, NMT models allow the encoding of a relevant context for each review, which narrows down the possible choices of words that the model has to choose from. Our NMT model had a perplexity of approximately $25$, while the model of \cite{yao2017automated} had a perplexity of approximately $90$ \footnote{Personal communication with the authors}. Second, the beam search in NMT models narrows down choices to natural-looking sentences. Third, we observed that the NMT model produced \emph{better structure} in the generated sentences (i.e. a more coherent story). \paragraph{Cost of generating reviews} With our setup, generating one review took less than one second. The cost of generation stems mainly from the overnight training. Assuming an electricity cost of 16 cents / kWh (California) and 8 hours of training, training the NMT model requires approximately 1.30 USD. This is a 90\% reduction in time compared to the state-of-the-art \cite{yao2017automated}. Furthermore, it is possible to generate both positive and negative reviews with the same model. \paragraph{Ease of customization} We experimented with inserting specific words into the text by increasing their log likelihoods in the beam search. We noticed that the success depended on the prevalence of the word in the training set. For example, adding a +5 to \emph{Mike} in the log-likelihood resulted in approximately 10\% prevalence of this word in the reviews. An attacker can therefore easily insert specific keywords to reviews, which can increase evasion probability. \paragraph{Ease of testing} Our diversification scheme is applicable during \emph{generation phase}, and does not affect the training setup of the network in any way. Once the NMT model is obtained, it is easy to obtain several different variants of NMT-Fake reviews by varying parameters $b$ and $\lambda$. \paragraph{Languages} The generation methodology is not per-se language-dependent. The requirement for successful generation is that sufficiently much data exists in the targeted language. However, our language model modifications require some knowledge of that target language's grammar to produce high-quality reviews. \paragraph{Generalizability of detection techniques} Currently, fake reviews are not universally detectable. Our results highlight that it is difficult to claim detection performance on unseen types of fake reviews (Section~\ref{sec:automated}). We see this an open problem that deserves more attention in fake reviews research. \paragraph{Generalizability to other types of datasets} Our technique can be applied to any dataset, as long as there is sufficient training data for the NMT model. We used approximately 2.9 million reviews for this work. \section{Conclusion} In this paper, we showed that neural machine translation models can be used to generate fake reviews that are very effective in deceiving even experienced, tech-savvy users.This supports anecdotal evidence \cite{national2017commission}.Our technique is more effective than state-of-the-art \cite{yao2017automated}.We conclude that machine-aided fake review detection is necessary since human users are ineffective in identifying fake reviews.We also showed that detectors trained using one type of fake reviews are not effective in identifying other types of fake reviews.Robust detection of fake reviews is thus still an open problem. \section*{Acknowledgments}We thank Tommi Gr\"{o}ndahl for assistance in planning user studies and theparticipants of the user study for their time and feedback. We also thankLuiza Sayfullina for comments that improved the manuscript.We thank the authors of \cite{yao2017automated} for answering questions abouttheir work. \bibliographystyle{splncs}\begin{thebibliography}{10} \bibitem{yao2017automated}Yao, Y., Viswanath, B., Cryan, J., Zheng, H., Zhao, B.Y.:\newblock Automated crowdturfing attacks and defenses in online review systems.\newblock In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, ACM (2017) \bibitem{murphy2012machine}Murphy, K.:\newblock Machine learning: a probabilistic approach.\newblock Massachusetts Institute of Technology (2012) \bibitem{challenge2013yelp}Yelp:\newblock {Yelp Challenge Dataset} (2013) \bibitem{mukherjee2013yelp}Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.:\newblock What yelp fake review filter might be doing?\newblock In: Seventh International AAAI Conference on Weblogs and Social Media (ICWSM). (2013) \bibitem{rayana2015collective}Rayana, S., Akoglu, L.:\newblock Collective opinion spam detection: Bridging review networks and metadata.\newblock In: {}Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining \bibitem{o2008user}{O'Connor}, P.:\newblock {User-generated content and travel: A case study on Tripadvisor.com}.\newblock Information and communication technologies in tourism 2008 (2008) \bibitem{luca2010reviews}Luca, M.:\newblock {Reviews, Reputation, and Revenue: The Case of Yelp. com}.\newblock {Harvard Business School} (2010) \bibitem{wang2012serf}Wang, G., Wilson, C., Zhao, X., Zhu, Y., Mohanlal, M., Zheng, H., Zhao, B.Y.:\newblock Serf and turf: crowdturfing for fun and profit.\newblock In: Proceedings of the 21st international conference on World Wide Web (WWW), ACM (2012) \bibitem{rinta2017understanding}Rinta-Kahila, T., Soliman, W.:\newblock Understanding crowdturfing: The different ethical logics behind the clandestine industry of deception.\newblock In: ECIS 2017: Proceedings of the 25th European Conference on Information Systems. (2017) \bibitem{luca2016fake}Luca, M., Zervas, G.:\newblock Fake it till you make it: Reputation, competition, and yelp review fraud.\newblock Management Science (2016) \bibitem{national2017commission}{National Literacy Trust}:\newblock Commission on fake news and the teaching of critical literacy skills in schools URL: \url{https://literacytrust.org.uk/policy-and-campaigns/all-party-parliamentary-group-literacy/fakenews/}. \bibitem{jurafsky2014speech}Jurafsky, D., Martin, J.H.:\newblock Speech and language processing. Volume~3.\newblock Pearson London: (2014) \bibitem{kingma2014adam}Kingma, D.P., Ba, J.:\newblock Adam: A method for stochastic optimization.\newblock arXiv preprint arXiv:1412.6980 (2014) \bibitem{cho2014learning}Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.:\newblock Learning phrase representations using rnn encoder--decoder for statistical machine translation.\newblock In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). (2014) \bibitem{klein2017opennmt}Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.:\newblock Opennmt: Open-source toolkit for neural machine translation.\newblock Proceedings of ACL, System Demonstrations (2017) \bibitem{wu2016google}Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et~al.:\newblock Google's neural machine translation system: Bridging the gap between human and machine translation.\newblock arXiv preprint arXiv:1609.08144 (2016) \bibitem{mei2017coherent}Mei, H., Bansal, M., Walter, M.R.:\newblock Coherent dialogue with attention-based language models.\newblock In: AAAI. (2017) 3252--3258 \bibitem{li2016diversity}Li, J., Galley, M., Brockett, C., Gao, J., Dolan, B.:\newblock A diversity-promoting objective function for neural conversation models.\newblock In: Proceedings of NAACL-HLT. (2016) \bibitem{rubin2006assessing}Rubin, V.L., Liddy, E.D.:\newblock Assessing credibility of weblogs.\newblock In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs. (2006) \bibitem{zhao2017news}news.com.au:\newblock {The potential of AI generated 'crowdturfing' could undermine online reviews and dramatically erode public trust} URL: \url{http://www.news.com.au/technology/online/security/the-potential-of-ai-generated-crowdturfing-could-undermine-online-reviews-and-dramatically-erode-public-trust/news-story/e1c84ad909b586f8a08238d5f80b6982}. \bibitem{pennebaker2015development}Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.:\newblock {The development and psychometric properties of LIWC2015}.\newblock Technical report (2015) \bibitem{honnibal-johnson:2015:EMNLP}Honnibal, M., Johnson, M.:\newblock An improved non-monotonic transition system for dependency parsing.\newblock In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), ACM (2015) \bibitem{bird2004nltk}Bird, S., Loper, E.:\newblock {NLTK: the natural language toolkit}.\newblock In: Proceedings of the ACL 2004 on Interactive poster and demonstration sessions, Association for Computational Linguistics (2004) \bibitem{kumar2018false}Kumar, S., Shah, N.:\newblock False information on web and social media: A survey.\newblock arXiv preprint arXiv:1804.08559 (2018) \bibitem{Everett2016Automated}Everett, R.M., Nurse, J.R.C., Erola, A.:\newblock The anatomy of online deception: What makes automated text convincing?\newblock In: Proceedings of the 31st Annual ACM Symposium on Applied Computing. SAC '16, ACM (2016) \end{thebibliography} \section*{Appendix} We present basic demographics of our MTurk study and the comparative study with experienced users in Table~\ref{table:amt_pop}. \begin{table}\caption{User study statistics.}\begin{center} \begin{tabular}{ | l | c | c | } \hline Quality & Mechanical Turk users & Experienced users\\ \hline Native English Speaker & Yes (20) & Yes (1) No (19) \\ Fluent in English & Yes (20) & Yes (20) \\ Age & 21-40 (17) 41-60 (3) & 21-25 (8) 26-30 (7) 31-35 (4) 41-45 (1)\\ Gender & Male (14) Female (6) & Male (17) Female (3)\\ Highest Education & High School (10) Bachelor (10) & Bachelor (9) Master (6) Ph.D. (5) \\ \hline \end{tabular} \label{table:amt_pop}\end{center}\end{table} Table~\ref{table:openNMT-py_commands} shows a listing of the openNMT-py commands we used to create our NMT model and to generate fake reviews. \begin{table}[t]\caption{Listing of used openNMT-py commands.}\begin{center} \begin{tabular}{ | l | l | } \hline Phase & Bash command \\ \hline Preprocessing & \begin{lstlisting}[language=bash]python preprocess.py -train_src context-train.txt-train_tgt reviews-train.txt -valid_src context-val.txt-valid_tgt reviews-val.txt -save_data model-lower -tgt_words_min_frequency 10\end{lstlisting} \\ & \\ Training & \begin{lstlisting}[language=bash]python train.py -data model -save_model model -epochs 8-gpuid 0 -learning_rate_decay 0.5 -optim adam-learning_rate 0.001 -start_decay_at 3\end{lstlisting} \\ & \\ Generation & \begin{lstlisting}[language=bash]python translate.py -model model_acc_35.54_ppl_25.68_e8.pt-src context-tst.txt -output pred-e8.txt -replace_unk-verbose -max_length 50 -gpu 0 \end{lstlisting} \\ \hline \end{tabular} \label{table:openNMT-py_commands}\end{center}\end{table} Table~\ref{table:MTurk_sub} shows the classification performance of Amazon Mechanical Turkers, separated across different categories of NMT-Fake reviews. The category with best performance ($b=0.3, \lambda=-5$) is denoted as NMT-Fake*. \begin{table}[b]\caption{MTurk study subclass classification reports. Classes are imbalanced in ratio 1:6. Random predictions are $p_\mathrm{human} = 86\%$ and $p_\mathrm{machine} = 14\%$, with $r_\mathrm{human} = r_\mathrm{machine} = 50\%$. Class-averaged F-scores for random predictions are $42\%$.}\begin{center} \begin{tabular}{ | c || c |c |c | c | } \hline $(b=0.3, \lambda = -3)$ & Precision & Recall & F-score & Support \\ \hline Human & 89\% & 63\% & 73\% & 994\\ NMT-Fake & 15\% & 45\% & 22\% & 146 \\ \hline \hline $(b=0.3, \lambda = -5)$ & Precision & Recall & F-score & Support \\ \hline Human & 86\% & 63\% & 73\% & 994\\ NMT-Fake* & 16\% & 40\% & 23\% & 171 \\ \hline \hline $(b=0.5, \lambda = -4)$ & Precision & Recall & F-score & Support \\ \hline Human & 88\% & 63\% & 73\% & 994\\ NMT-Fake & 21\% & 55\% & 30\% & 181 \\ \hline \hline $(b=0.7, \lambda = -3)$ & Precision & Recall & F-score & Support \\ \hline Human & 88\% & 63\% & 73\% & 994\\ NMT-Fake & 19\% & 50\% & 27\% & 170 \\ \hline \hline $(b=0.7, \lambda = -5)$ & Precision & Recall & F-score & Support \\ \hline Human & 89\% & 63\% & 74\% & 994\\ NMT-Fake & 21\% & 57\% & 31\% & 174 \\ \hline \hline $(b=0.9, \lambda = -4)$ & Precision & Recall & F-score & Support \\ \hline Human & 88\% & 63\% & 73\% & 994\\ NMT-Fake & 18\% & 50\% & 27\% & 164 \\ \hline \end{tabular} \label{table:MTurk_sub}\end{center}\end{table} Figure~\ref{fig:screenshot} shows screenshots of the first two pages of our user study with experienced participants. \begin{figure}[ht]\centering\includegraphics[width=1.\columnwidth]{figures/screenshot_7-3.png}\caption{Screenshots of the first two pages in the user study. Example 1 is a NMT-Fake* review, the rest are human-written.}\label{fig:screenshot}\end{figure} Table~\ref{table:features_adaboost} shows the features used to detect NMT-Fake reviews using the AdaBoost classifier. \begin{table}\caption{Features used in NMT-Fake review detector.}\begin{center} \begin{tabular}{ | l | c | } \hline Feature type & Number of features \\ \hline \hline Readability features & 13 \\ \hline Unique POS tags & $~20$ \\ \hline Word unigrams & 22,831 \\ \hline 1/2/3/4-grams of simple part-of-speech tags & 54,240 \\ \hline 1/2/3-grams of detailed part-of-speech tags & 112,944 \\ \hline 1/2/3-grams of syntactic dependency tags & 93,195 \\ \hline \end{tabular} \label{table:features_adaboost}\end{center}\end{table} \end{document} | [
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in Data Studio
Qasper (Question Answering on Scientific Research Papers)
This dataset card aims to be a base template for QasperQA dataset released by Allenai. It has been generated using this raw template. This is a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers.
Dataset Details - Abstract of the Paper
Readers of academic research papers often read with the goal of answering specific ques- tions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that reflect the difficulty of the task arising from complex reasoning about claims made in multiple parts of a paper. In contrast, existing information- seeking question answering datasets usually contain questions about generic factoid-type information. We therefore present QASPER, a dataset of 5,049 questions over 1,585 Natu- ral Language Processing papers. Each ques- tion is written by an NLP practitioner who read only the title and abstract of the corre- sponding paper, and the question seeks infor- mation present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting ev- idence to answers. We find that existing mod- els that do well on other QA tasks do not per- form well on answering these questions, un- derperforming humans by at least 27 F1 points when answering them from entire papers, motivating further research in document-grounded, information-seeking QA, which our dataset is designed to facilitate.
Dataset Description
- Curated by: Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A. Smith, Matt Gardner
- Shared by: Allen Institute for AI, Paul G. Allen School of CSE, University of Washington
Dataset Sources
- Repository: GitHub Repo
- Paper: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Dataset Structure
{ context: , questions: [question1, question2, ...], asnwers: [[answer1_to_question1, answer2_to_question1, ...], [answer1_to_question2, answer2_to_question2, ...], ...] }
Citation
BibTeX:
@misc{dasigi2021datasetinformationseekingquestionsanswers, title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers}, author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner}, year={2021}, eprint={2105.03011}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2105.03011}, }
Dataset Card Author
Hulki Çıray, researcher at GGLab, Linkedin, Hugging Face
Dataset Card Contact
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