This system is based on a supervised learning approach that considers two kinds of attributes . On the one hand , some attributes indicating the textual entailment between the given support text and the hypothesis constructed from the question and answer . On the other hand , some new features denoting certain answer restrictions as imposed by the question ’s type and format . The aim of this paper is to propose a method of simulating - in a syntactico-semantic parser - the behaviour of semantic roles in case of a language that has no resources such as VerbNet of FrameNet , but has relatively rich morphosyntax ( here : Polish ) . We argue that using an approximation of semantic roles derived from syntactic ( grammatical functions ) and morphosyntactic ( grammatical cases ) features of arguments may be beneficial for applications such as text entailment . Similarity between natural language texts , sentences in terms of meaning , known as textual entailment , is a generic problem in the area of computational linguistics . In the last few years researchers worked on various aspects of textual entailment problem , but mostly restricted to English language . Here in this paper we present a method for measuring the semantic similarity of Bengali tweets using WordNet . In this talk I suggest that computational modeling of the semantic level should also focus on encapsulating the various processing tasks within a unified module ( engine ) . The input/output specification of such engine ( API ) can be based on the textual entailment paradigm , which will be described in brief and suggested as an attractive framework for applied semantic inference . The talk will illustrate an initial proposal for the engine 's API , designed to be embedded within the prominent language processing applications . Stop words are removed from each H and query words are identified to retrieve the most relevant sentences from the associated document using Lucene . Relevant sentences are retrieved from the associated document based on the TF-IDF of the matching query words along with n-gram overlap of the sentence with the H. Each retrieved sentence defines the Text T. Each T-H pair is assigned a ranking score in the AV system that works on textual entailment principle . The answer option for which the TH pair gets the maximum score is selected as the possible answer . We present a graph-based semi-supervised learning for the question-answering ( QA ) task for ranking candidate sentences . Using textual entailment analysis , we obtain entailment scores between a natural language question posed by the user and the candidate sentences returned from search engine . The textual entailment between two sentences is assessed via features representing high-level attributes of the entailment problem such as sentence structure matching , question-type named-entity matching based on a question-classifier , etc . Finally , textual entailment presents a wide array of approaches to potential end users with little guidance on which to pick . Our contribution to this situation is the novel EXCITEMENT architecture , which was developed to enable and encourage the consolidation of methods and resources in the textual entailment area . It decomposes RTE into components with strongly typed interfaces . This paper presents a Recognizing Textual Entailment system which uses semantic distances to sentence level over WordNet to assess the impact on predicting Textual Entailment datasets . We extent word-to-word metrics to sentence level in order to best fit in textual entailment domain . Finally , we show experiments over several RTE datasets and draw conclusions about the useful of WordNet semantic measures on this task . Recognizing Textual Entailment ( RTE ) as one of the fundamental problems in those natural language processing applications has attracted increasing attention in recent years . This paper proposes a new method for textual entailment measure which is based on lexical , shallow syntactic analysis combined with fuzzy set theory . Further we model lexical and semantic features based on this method and perform textual entailment recognition using machine learning algorithm . k trained biases that parameterize the gates and transformations of the input , σ denotes the element - wise application of the sigmoid function and the element - wise multiplication of two vectors . section : RECOGNIZING TEXTUAL ENTAILMENT WITH LSTMS LSTMs can readily be used for RTE by independently encoding the premise and hypothesis as dense vectors and taking their concatenation as input to an MLP classifier document : Order - Embeddings of Images and Language Hypernymy , textual entailment , and image captioning can be seen as special cases of a single visual - semantic hierarchy over words , sentences , and images . In this paper we advocate for explicitly modeling the partial order structure of this hierarchy . document : Grounded Textual Entailment Capturing semantic relations between sentences , such as entailment , is a long - standing challenge for computational semantics . Recurrent neural networks ( RNNs ) have been successful for modeling sequence data . RNNs equipped with gated hidden units and internal short - term memories , such as long short - term memories ( LSTM ) have achieved a notable success in several NLP tasks including named entity recognition , constituency parsing , textual entailment recognition , question answering , and machine translation . However , most LSTM models explored so far are sequential . The increase of 4.7 % with ELMo is also significantly larger then the 1.8 % improvement from adding CoVe to a baseline model McCann2017LearnedIT . Textual entailment Textual entailment is the task of determining whether a “ hypothesis ” is true , given a “ premise ” . The Stanford Natural Language Inference ( SNLI ) corpus snliemnlp2015 provides approximately 550 K hypothesis / premise pairs . In this paper , we look at Natural Language Inference , arguing that the notion of inference the current NLP systems are learning is much narrower compared to the range of inference patterns found in human reasoning . We take a look at the history and the nature of creating datasets for NLI . We discuss the datasets that are mainly used today for the relevant tasks and show why those are not enough to generalize to other reasoning tasks , e.g. logical and legal reasoning , or reasoning in dialogue settings . Many recent studies have shown that for models trained on datasets for natural language inference ( NLI ) , it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise . In this work , we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias . Further , we continue to train the predictive models with our swapping manner and propose to use the deviation of the model 's evaluation performances under different percentages of training text fragments to be swapped to describe the robustness of a predictive model . Our evaluation metrics leads to some interesting understandings of recent published NLI methods . Finally , we also apply the swapping operation on NLI models to see the effectiveness of this straightforward method in mitigating the confounding factor problems in training generic sentence embeddings for other NLP transfer tasks . Its Natural Language Generation ( NLG ) module masks the formulation of queries as the composition of English text and generates descriptions of query answers . An important feature of Quelo NLI is that it is portable as it is not bound to an ontology of a specific domain . We describe Quelo NLI functionality and present a grammar-based natural language generation approach that better supports the domain-independent generation of fluent queries and naturally extends for the generation of answers descriptions . We concentrate on describing the generation resources , namely a domain-independent hand-written grammar and a lexicon that is automatically extracted from concepts and relations of the underlying ontology . While , the computation of the NLI is well addressed by mathematical models based on the knowledge of parameters and spectral load of fiber spans . So , the NLI prediction is mainly impaired by the uncertainties on insertion losses an spectral tilting . An accurate and spectrally resolved GSNR estimation enables to optimize the power control and to reliably and automatically deploy lightpaths with minimum margin , consequently maximizing the transmission capacity . With thousands of articles published daily , there is a need to detect inconsistent and often contradictory findings . Natural language inference ( NLI ) gained a lot of interest in the past years , however , domain-specific NLI systems are yet to be examined in depth . In this paper , we conduct several experiments on sentence pairs extracted from PubMed abstracts , to infer whether they express entailment , contradiction or neutral meanings . For five years the End-User Friendly Interface to Data management ( EUFID ) project team at System Development Corporation worked on the design and implementation of a Natural-Language Interface ( NLI ) system that was to be independent of both the application and the database management system . In this paper we describe application , natural-language and database management problems involved in NLI development , with specific reference to the EUFID system as an example . A human-like dialogue agent should not only respond naturally , but also maintain a consistent persona . In this paper , we exploit the advantages of natural language inference ( NLI ) technique to address the issue of generating persona consistent dialogues . Different from existing work that re-ranks the retrieved responses through an NLI model , we cast the task as a reinforcement learning problem and propose to exploit the NLI signals from response-persona pairs as rewards for the process of dialogue generation . section : Introduction Natural language inference ( NLI ) refers to the problem of determining entailment and contradiction relationships between a premise and a hypothesis . NLI is a central problem in language understanding and recently the large SNLI corpus of 570 K sentence pairs was created for this task . subsection : Natural Language Inference For the evaluation of performance of the proposed method on the NLI task , SNLI and MultiNLI datasets are used . The objective of both datasets is to predict the relationship between a premise and a hypothesis sentence : entailment , contradiction , and neutral . The NLI task defined here involves a premise of words and a hypothesis of words , and aims to find a logical relationship between and . The design of the output module follows the answer module of the stochastic answer network ( SAN ) liu2018san4nli , a state - of - the - art neural NLI model . SAN ’s answer module uses multi - step reasoning . Let represent words in the source sentence . We assume is a word embedding vector which are initialized using some pre - trained vector embeddings ( and is then fine - tuned end - to - end via the NLI supervision ) . Then , the input of th biLSTM layer at time is defined as : where represents vector concatenation . Then , assuming we have layers of biLSTM , the final vector representation will be obtained by applying row - max - pool over the output of the last biLSTM layer , similar to conneau2017supervised . The final layer is defined as : where , , is the dimension of the hidden state of the last forward and backward LSTM layers , and is the final vector representation for the source sentence ( which is later fed to the NLI classifier ) . The closest encoder architecture to ours is that of conneau2017supervised , whose model consists of a single - layer biLSTM with a max - pooling layer , which we treat as our starting point . section : Introduction Natural language inference ( NLI ) is an important and significant task in natural language processing ( NLP ) . It concerns whether a hypothesis can be inferred from a premise , requiring understanding of the semantic similarity between the hypothesis and the premise to discriminate their relation . Our ultimate objective is to provide an empirical evaluation of learning - centered approaches to NLI , advancing the case for NLI as a tool for the evaluation of domain - general approaches to semantic representation . However , in our view , existing NLI corpora do not permit such an assessment . They are generally too small for training modern data - intensive , wide - coverage models , many contain sentences that were algorithmically generated , and they are often beset with indeterminacies of event and entity coreference that significantly impact annotation quality . If there was no such consensus , which occurred in about 2 % of cases , we assigned the placeholder label ‘ - ’ . While these unlabeled examples are included in the corpus distribution , they are unlikely to be helpful for the standard NLI classification task , and we do not include them in either training or evaluation in the experiments that we discuss in this paper . The results of this validation process are summarized in Table [ reference ] . The Fleiss scores ( computed over every example with a full five annotations ) are likely to be conservative given our large and unevenly distributed pool of annotators , but they still provide insights about the levels of disagreement across the three semantic classes . This disagreement likely reflects not just the limitations of large crowdsourcing efforts but also the uncertainty inherent in naturalistic NLI . Regardless , the overall rate of agreement is extremely high , suggesting that the corpus is sufficiently high quality to pose a challenging but realistic machine learning task . Our ability to evaluate standard classifier - base NLI models , however , was limited to those which were designed to scale to SNLI ’s size without modification , so a more complete comparison of approaches will have to wait for future work . In this section , we explore the performance of three classes of models which could scale readily : ( i ) models from a well - known NLI system , the Excitement Open Platform ; ( ii ) variants of a strong but simple feature - based classifier model , which makes use of both unlexicalized and lexicalized features , and ( iii ) distributed representation models , including a baseline model and neural network sequence models . subsection : Excitement Open Platform models Natural Language Inference ( NLI ) is a fundamental step towards natural language understanding . The task aims to detect whether a premise entails or contradicts a given hypothesis . This is illustrated through examples from a system under development to present parts of ontologies in natural language . Natural language directed inference represents an interesting challenge to research in automated reasoning and natural language processing . The domain mismatch between the entailment ( captions ) and summarization ( news ) datasets sug-ive summarization , the task of rewriting and compressing a document into a short summary , has achieved considerable success with neural sequence-tosequence models . However , these models can still benefit from stronger natural language inference skills , since a correct summary is logically entailed by the input document , i.e. , it should not contain any contradictory or unrelated information . We incorporate such knowledge into an abstractive summarization model via multi-task learning , where we share its decoder parameters with those of an entailment generation model . Automatic inference from natural language is a critical yet challenging problem for many language-related applications . To improve the ability of natural language inference for computer systems ; recent years have seen an increasing research effort on textual entailment . Here , we introduce a visualization system that , through a tight yet flexible integration between visualization elements and the underlying model , allows a user to interrogate the model by perturbing the input , internal state , and prediction while observing changes in other parts of the pipeline . We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model , probe its inner states , and interpret and form hypotheses about fundamental model mechanisms such as attention . In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference . Our approach is aligned to mimic how a human does the natural language inference process given two statements . The model uses variants of Long Short Term Memory ( LSTM ) , attention mechanism and composable neural networks , to carry out the task . Recognizing Textual Entailment , also known as Natural Language Inference recognition , aims to identify if it is possible to infer the meaning of a text from another fragment of text . In this work , we investigate the use of multilingual models , through BERT , for recognizing inference and similarity in the ASSIN 2 dataset , an entailment recognition and sentence similarity corpus for Portuguese . One of the reasonable ways to overcome the limitations is the semantic web-based business support framework . Especially , in this study , we focused on the development of semantic web ontology and natural language (NL)-based inference framework . To validate the efficiency of the proposed framework , we considered a reasonable scenario for course recommendation in a university . FrameNet is a lexicographic resource that captures syntactic and semantic generalizations that go beyond surface form and part of speech , famously including the relationships among words like buy , sell , purchase and price . These rich structural relations provide an attractive foundation for work in deeper natural language understanding and inference , as attested by the breadth of applications at the Workshop in Honor of Chuck Fillmore at ACL 2014 ( Petruck and de Melo , 2014 ) . But FrameNet was not designed to support scalable language technologies ; indeed , it is perhaps a paradigm example of a hand-curated knowledge resource , one that has required significant expertise , training , time and expense to create and that remains under development . section : Conclusion We explored various simple combinations and connections of biLSTM - RNN layered architectures and developed a Shortcut - Stacked Sentence Encoder for natural language inference . Our model is the top single result in the EMNLP RepEval 2017 The dataset contains 20 , 360 / 2 , 733 / 6 , 165 QA pairs for train / dev / test sets . We used the same setup in the language inference task except that we replace the layer with a layer and model the following conditional probability distribution . where and are the question and the answer encoded vectors and denotes the output of the hidden layer of the MLP . If not , how can neural - network - based NLI models benefit from external knowledge and how to build NLI models to leverage it ? In this paper , we enrich the state - of - the - art neural natural language inference models with external knowledge . We demonstrate that the proposed models improve neural NLI models to achieve the state - of - the - art performance on the SNLI and MultiNLI datasets . document : A large annotated corpus for learning natural language inference Understanding entailment and contradiction is fundamental to understanding natural language , and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations . At 570 K pairs , it is two orders of magnitude larger than all other resources of its type . This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models , and it allows a neural network - based model to perform competitively on natural language inference benchmarks for the first time . section : Introduction To date , the primary sources of annotated NLI corpora have been the Recognizing Textual Entailment ( RTE ) challenge tasks . These are generally high - quality , hand - labeled data sets , and they have stimulated innovative logical and statistical models of natural language reasoning , but their small size ( fewer than a thousand examples each ) limits their utility as a testbed for learned distributed representations . The data for the SemEval 2014 task called Sentences Involving Compositional Knowledge ( SICK ) is a step up in terms of size , but only to 4 , 500 training examples , and its partly automatic construction introduced some spurious patterns into the data ( ? , 6 ) . Therefore , we can comprehend the output of g θ reveals the relation between the object pair and the question and f φ aggregates all these outputs to infer the answer . bAbI story - based QA dataset bAbI story - based QA dataset is composed of 20 different types of tasks for testing natural language reasoning ability . Each task requires different methods to infer the answer . The problem of recognizing textual entailment ( RTE ) has been recently addressed using syntactic and lexical models with some success . Here , we further explore this problem , this time using the world knowledge captured in large semantic graphs such as WordNet . A key challenge at the core of many Natural Language Processing ( NLP ) tasks is the ability to determine which conclusions can be inferred from a given natural language text . This problem , called the Recognition of Textual Entailment ( RTE ) , has initiated the development of a range of algorithms , methods , and technologies . Unfortunately , research on Textual Entailment ( TE ) , like semantics research more generally , is fragmented into studies focussing on various aspects of semantics such as world knowledge , lexical and syntactic relations , or more specialized kinds of inference . We proved the correctness of the new algorithm and established its efficiency analytically and empirically . The utility of our approach was illustrated on two tasks : unsupervised relation extraction from a large corpus , and the Recognizing Textual Entailment ( RTE ) benchmarks . We introduce a new semantic annotation scheme for the Recognizing Textual Entailment ( RTE ) dataset as well as a manually annotated dataset that uses this scheme . The scheme addresses three types of modification that license entailment patterns : restrictive , appositive and conjunctive , with a formal semantic specification of these patterns ' contribution for establishing entailment . Unlike many previous lexically-based systems , we do not measure the degree of overlap or similarity , and we do no machine learning . This simple system performs well on the Recognizing Textual Entailment ( RTE ) evaluation . This was one of the motivations for the first Answer Validation Exercise at CLEF 2006 ( AVE 2006 ) . The starting point for the AVE 2006 was the reformulation of the answer validation as a recognizing textual entailment ( RTE ) problem , under the assumption that a hypothesis can be automatically generated instantiating a hypothesis pattern with a QA system answer . The test collections that we developed in seven different languages at AVE 2006 are specially oriented to the development and evaluation of answer validation systems . We applied various WordNet based similarity measures to the RTE ( Recognizing Textual Entailment ) task in order to compare the effects of them on Textual Entailment Recognition . Although the improvements over a baseline system are not big , many of them show positive effects . section : Introduction and Background Natural language inference ( NLI ) or recognizing textual entailment ( RTE ) is a fundamental semantic task in the field of natural language processing . The problem is to determine whether a given hypothesis sentence can be logically inferred from a given premise sentence . After formal semanticists developed FraCas in the mid ’ 90 , an increase in statistical approaches to computational semantics gave rise to the need for suitable evaluation datasets . Hence , Recognizing Textual Entailment ( RTE ) shared tasks have been organized regularly . Recent work on compositional distributional models has motivated the development of the SICK dataset of sentence pairs in entailment relations for evaluating such models . Ourmodelsupportsbatched computation for a speedup of up to 25 ◊ over other tree-structured models , and its integrated parser can operate on unparsed data with little loss in accuracy . We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence-encoding models . Our models are trained in a regression framework , and can easily incorporate a rich set of linguistic features . The performance of our edit distance based models is contrasted with an adaptation of the Stanford textual entailment system to the STS task . Our results show that the most advanced edit distance model , pPDA , outperforms our entailment system on all but one of the genres included in the STS task . Our model supports batched computation for a speedup of up to 25 over other tree - structured models , and its integrated parser can operate on unparsed data with little loss in accuracy . We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence - encoding models . section : Introduction This model is a basically plausible model of human sentence processing and yields substantial accuracy gains over pure sequence - or tree - based models . We evaluate SPINN on the Stanford Natural Language Inference entailment task , and find that it significantly outperforms other sentence - encoding - based models , even with a relatively simple and underpowered implementation of the built - in parser . We also find that SPINN yields speed increases of up to 25 over a standard TreeRNN implementation .
This system is based on a supervised learning approach that considers two kinds of attributes . On the one hand , some attributes indicating the textual entailment between the given support text and the hypothesis constructed from the question and answer . On the other hand , some new features denoting certain answer restrictions as imposed by the question ’s type and format .
The aim of this paper is to propose a method of simulating - in a syntactico-semantic parser - the behaviour of semantic roles in case of a language that has no resources such as VerbNet of FrameNet , but has relatively rich morphosyntax ( here : Polish ) . We argue that using an approximation of semantic roles derived from syntactic ( grammatical functions ) and morphosyntactic ( grammatical cases ) features of arguments may be beneficial for applications such as text entailment .
Similarity between natural language texts , sentences in terms of meaning , known as textual entailment , is a generic problem in the area of computational linguistics . In the last few years researchers worked on various aspects of textual entailment problem , but mostly restricted to English language . Here in this paper we present a method for measuring the semantic similarity of Bengali tweets using WordNet .
In this talk I suggest that computational modeling of the semantic level should also focus on encapsulating the various processing tasks within a unified module ( engine ) . The input/output specification of such engine ( API ) can be based on the textual entailment paradigm , which will be described in brief and suggested as an attractive framework for applied semantic inference . The talk will illustrate an initial proposal for the engine 's API , designed to be embedded within the prominent language processing applications .
Stop words are removed from each H and query words are identified to retrieve the most relevant sentences from the associated document using Lucene . Relevant sentences are retrieved from the associated document based on the TF-IDF of the matching query words along with n-gram overlap of the sentence with the H. Each retrieved sentence defines the Text T. Each T-H pair is assigned a ranking score in the AV system that works on textual entailment principle . The answer option for which the TH pair gets the maximum score is selected as the possible answer .
We present a graph-based semi-supervised learning for the question-answering ( QA ) task for ranking candidate sentences . Using textual entailment analysis , we obtain entailment scores between a natural language question posed by the user and the candidate sentences returned from search engine . The textual entailment between two sentences is assessed via features representing high-level attributes of the entailment problem such as sentence structure matching , question-type named-entity matching based on a question-classifier , etc .
Finally , textual entailment presents a wide array of approaches to potential end users with little guidance on which to pick . Our contribution to this situation is the novel EXCITEMENT architecture , which was developed to enable and encourage the consolidation of methods and resources in the textual entailment area . It decomposes RTE into components with strongly typed interfaces .
This paper presents a Recognizing Textual Entailment system which uses semantic distances to sentence level over WordNet to assess the impact on predicting Textual Entailment datasets . We extent word-to-word metrics to sentence level in order to best fit in textual entailment domain . Finally , we show experiments over several RTE datasets and draw conclusions about the useful of WordNet semantic measures on this task .
Recognizing Textual Entailment ( RTE ) as one of the fundamental problems in those natural language processing applications has attracted increasing attention in recent years . This paper proposes a new method for textual entailment measure which is based on lexical , shallow syntactic analysis combined with fuzzy set theory . Further we model lexical and semantic features based on this method and perform textual entailment recognition using machine learning algorithm .
k trained biases that parameterize the gates and transformations of the input , σ denotes the element - wise application of the sigmoid function and the element - wise multiplication of two vectors . section : RECOGNIZING TEXTUAL ENTAILMENT WITH LSTMS LSTMs can readily be used for RTE by independently encoding the premise and hypothesis as dense vectors and taking their concatenation as input to an MLP classifier
document : Order - Embeddings of Images and Language Hypernymy , textual entailment , and image captioning can be seen as special cases of a single visual - semantic hierarchy over words , sentences , and images . In this paper we advocate for explicitly modeling the partial order structure of this hierarchy .
document : Grounded Textual Entailment Capturing semantic relations between sentences , such as entailment , is a long - standing challenge for computational semantics .
Recurrent neural networks ( RNNs ) have been successful for modeling sequence data . RNNs equipped with gated hidden units and internal short - term memories , such as long short - term memories ( LSTM ) have achieved a notable success in several NLP tasks including named entity recognition , constituency parsing , textual entailment recognition , question answering , and machine translation . However , most LSTM models explored so far are sequential .
The increase of 4.7 % with ELMo is also significantly larger then the 1.8 % improvement from adding CoVe to a baseline model McCann2017LearnedIT . Textual entailment Textual entailment is the task of determining whether a “ hypothesis ” is true , given a “ premise ” . The Stanford Natural Language Inference ( SNLI ) corpus snliemnlp2015 provides approximately 550 K hypothesis / premise pairs .
In this paper , we look at Natural Language Inference , arguing that the notion of inference the current NLP systems are learning is much narrower compared to the range of inference patterns found in human reasoning . We take a look at the history and the nature of creating datasets for NLI . We discuss the datasets that are mainly used today for the relevant tasks and show why those are not enough to generalize to other reasoning tasks , e.g. logical and legal reasoning , or reasoning in dialogue settings .
Many recent studies have shown that for models trained on datasets for natural language inference ( NLI ) , it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise . In this work , we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias .
Further , we continue to train the predictive models with our swapping manner and propose to use the deviation of the model 's evaluation performances under different percentages of training text fragments to be swapped to describe the robustness of a predictive model . Our evaluation metrics leads to some interesting understandings of recent published NLI methods . Finally , we also apply the swapping operation on NLI models to see the effectiveness of this straightforward method in mitigating the confounding factor problems in training generic sentence embeddings for other NLP transfer tasks .
Its Natural Language Generation ( NLG ) module masks the formulation of queries as the composition of English text and generates descriptions of query answers . An important feature of Quelo NLI is that it is portable as it is not bound to an ontology of a specific domain . We describe Quelo NLI functionality and present a grammar-based natural language generation approach that better supports the domain-independent generation of fluent queries and naturally extends for the generation of answers descriptions . We concentrate on describing the generation resources , namely a domain-independent hand-written grammar and a lexicon that is automatically extracted from concepts and relations of the underlying ontology .
While , the computation of the NLI is well addressed by mathematical models based on the knowledge of parameters and spectral load of fiber spans . So , the NLI prediction is mainly impaired by the uncertainties on insertion losses an spectral tilting . An accurate and spectrally resolved GSNR estimation enables to optimize the power control and to reliably and automatically deploy lightpaths with minimum margin , consequently maximizing the transmission capacity .
With thousands of articles published daily , there is a need to detect inconsistent and often contradictory findings . Natural language inference ( NLI ) gained a lot of interest in the past years , however , domain-specific NLI systems are yet to be examined in depth . In this paper , we conduct several experiments on sentence pairs extracted from PubMed abstracts , to infer whether they express entailment , contradiction or neutral meanings .
For five years the End-User Friendly Interface to Data management ( EUFID ) project team at System Development Corporation worked on the design and implementation of a Natural-Language Interface ( NLI ) system that was to be independent of both the application and the database management system . In this paper we describe application , natural-language and database management problems involved in NLI development , with specific reference to the EUFID system as an example .
A human-like dialogue agent should not only respond naturally , but also maintain a consistent persona . In this paper , we exploit the advantages of natural language inference ( NLI ) technique to address the issue of generating persona consistent dialogues . Different from existing work that re-ranks the retrieved responses through an NLI model , we cast the task as a reinforcement learning problem and propose to exploit the NLI signals from response-persona pairs as rewards for the process of dialogue generation .
section : Introduction Natural language inference ( NLI ) refers to the problem of determining entailment and contradiction relationships between a premise and a hypothesis . NLI is a central problem in language understanding and recently the large SNLI corpus of 570 K sentence pairs was created for this task .
subsection : Natural Language Inference For the evaluation of performance of the proposed method on the NLI task , SNLI and MultiNLI datasets are used . The objective of both datasets is to predict the relationship between a premise and a hypothesis sentence : entailment , contradiction , and neutral .
The NLI task defined here involves a premise of words and a hypothesis of words , and aims to find a logical relationship between and . The design of the output module follows the answer module of the stochastic answer network ( SAN ) liu2018san4nli , a state - of - the - art neural NLI model . SAN ’s answer module uses multi - step reasoning .
Let represent words in the source sentence . We assume is a word embedding vector which are initialized using some pre - trained vector embeddings ( and is then fine - tuned end - to - end via the NLI supervision ) . Then , the input of th biLSTM layer at time is defined as : where represents vector concatenation . Then , assuming we have layers of biLSTM , the final vector representation will be obtained by applying row - max - pool over the output of the last biLSTM layer , similar to conneau2017supervised . The final layer is defined as : where , , is the dimension of the hidden state of the last forward and backward LSTM layers , and is the final vector representation for the source sentence ( which is later fed to the NLI classifier ) . The closest encoder architecture to ours is that of conneau2017supervised , whose model consists of a single - layer biLSTM with a max - pooling layer , which we treat as our starting point .
section : Introduction Natural language inference ( NLI ) is an important and significant task in natural language processing ( NLP ) . It concerns whether a hypothesis can be inferred from a premise , requiring understanding of the semantic similarity between the hypothesis and the premise to discriminate their relation .
Our ultimate objective is to provide an empirical evaluation of learning - centered approaches to NLI , advancing the case for NLI as a tool for the evaluation of domain - general approaches to semantic representation . However , in our view , existing NLI corpora do not permit such an assessment . They are generally too small for training modern data - intensive , wide - coverage models , many contain sentences that were algorithmically generated , and they are often beset with indeterminacies of event and entity coreference that significantly impact annotation quality .
If there was no such consensus , which occurred in about 2 % of cases , we assigned the placeholder label ‘ - ’ . While these unlabeled examples are included in the corpus distribution , they are unlikely to be helpful for the standard NLI classification task , and we do not include them in either training or evaluation in the experiments that we discuss in this paper . The results of this validation process are summarized in Table [ reference ] .
The Fleiss scores ( computed over every example with a full five annotations ) are likely to be conservative given our large and unevenly distributed pool of annotators , but they still provide insights about the levels of disagreement across the three semantic classes . This disagreement likely reflects not just the limitations of large crowdsourcing efforts but also the uncertainty inherent in naturalistic NLI . Regardless , the overall rate of agreement is extremely high , suggesting that the corpus is sufficiently high quality to pose a challenging but realistic machine learning task .
Our ability to evaluate standard classifier - base NLI models , however , was limited to those which were designed to scale to SNLI ’s size without modification , so a more complete comparison of approaches will have to wait for future work . In this section , we explore the performance of three classes of models which could scale readily : ( i ) models from a well - known NLI system , the Excitement Open Platform ; ( ii ) variants of a strong but simple feature - based classifier model , which makes use of both unlexicalized and lexicalized features , and ( iii ) distributed representation models , including a baseline model and neural network sequence models . subsection : Excitement Open Platform models
Natural Language Inference ( NLI ) is a fundamental step towards natural language understanding . The task aims to detect whether a premise entails or contradicts a given hypothesis .
This is illustrated through examples from a system under development to present parts of ontologies in natural language . Natural language directed inference represents an interesting challenge to research in automated reasoning and natural language processing .
The domain mismatch between the entailment ( captions ) and summarization ( news ) datasets sug-ive summarization , the task of rewriting and compressing a document into a short summary , has achieved considerable success with neural sequence-tosequence models . However , these models can still benefit from stronger natural language inference skills , since a correct summary is logically entailed by the input document , i.e. , it should not contain any contradictory or unrelated information . We incorporate such knowledge into an abstractive summarization model via multi-task learning , where we share its decoder parameters with those of an entailment generation model .
Automatic inference from natural language is a critical yet challenging problem for many language-related applications . To improve the ability of natural language inference for computer systems ; recent years have seen an increasing research effort on textual entailment .
Here , we introduce a visualization system that , through a tight yet flexible integration between visualization elements and the underlying model , allows a user to interrogate the model by perturbing the input , internal state , and prediction while observing changes in other parts of the pipeline . We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model , probe its inner states , and interpret and form hypotheses about fundamental model mechanisms such as attention .
In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference . Our approach is aligned to mimic how a human does the natural language inference process given two statements . The model uses variants of Long Short Term Memory ( LSTM ) , attention mechanism and composable neural networks , to carry out the task .
Recognizing Textual Entailment , also known as Natural Language Inference recognition , aims to identify if it is possible to infer the meaning of a text from another fragment of text . In this work , we investigate the use of multilingual models , through BERT , for recognizing inference and similarity in the ASSIN 2 dataset , an entailment recognition and sentence similarity corpus for Portuguese .
One of the reasonable ways to overcome the limitations is the semantic web-based business support framework . Especially , in this study , we focused on the development of semantic web ontology and natural language (NL)-based inference framework . To validate the efficiency of the proposed framework , we considered a reasonable scenario for course recommendation in a university .
FrameNet is a lexicographic resource that captures syntactic and semantic generalizations that go beyond surface form and part of speech , famously including the relationships among words like buy , sell , purchase and price . These rich structural relations provide an attractive foundation for work in deeper natural language understanding and inference , as attested by the breadth of applications at the Workshop in Honor of Chuck Fillmore at ACL 2014 ( Petruck and de Melo , 2014 ) . But FrameNet was not designed to support scalable language technologies ; indeed , it is perhaps a paradigm example of a hand-curated knowledge resource , one that has required significant expertise , training , time and expense to create and that remains under development .
section : Conclusion We explored various simple combinations and connections of biLSTM - RNN layered architectures and developed a Shortcut - Stacked Sentence Encoder for natural language inference . Our model is the top single result in the EMNLP RepEval 2017
The dataset contains 20 , 360 / 2 , 733 / 6 , 165 QA pairs for train / dev / test sets . We used the same setup in the language inference task except that we replace the layer with a layer and model the following conditional probability distribution . where and are the question and the answer encoded vectors and denotes the output of the hidden layer of the MLP .
If not , how can neural - network - based NLI models benefit from external knowledge and how to build NLI models to leverage it ? In this paper , we enrich the state - of - the - art neural natural language inference models with external knowledge . We demonstrate that the proposed models improve neural NLI models to achieve the state - of - the - art performance on the SNLI and MultiNLI datasets .
document : A large annotated corpus for learning natural language inference Understanding entailment and contradiction is fundamental to understanding natural language , and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations .
At 570 K pairs , it is two orders of magnitude larger than all other resources of its type . This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models , and it allows a neural network - based model to perform competitively on natural language inference benchmarks for the first time . section : Introduction
To date , the primary sources of annotated NLI corpora have been the Recognizing Textual Entailment ( RTE ) challenge tasks . These are generally high - quality , hand - labeled data sets , and they have stimulated innovative logical and statistical models of natural language reasoning , but their small size ( fewer than a thousand examples each ) limits their utility as a testbed for learned distributed representations . The data for the SemEval 2014 task called Sentences Involving Compositional Knowledge ( SICK ) is a step up in terms of size , but only to 4 , 500 training examples , and its partly automatic construction introduced some spurious patterns into the data ( ? , 6 ) .
Therefore , we can comprehend the output of g θ reveals the relation between the object pair and the question and f φ aggregates all these outputs to infer the answer . bAbI story - based QA dataset bAbI story - based QA dataset is composed of 20 different types of tasks for testing natural language reasoning ability . Each task requires different methods to infer the answer .
The problem of recognizing textual entailment ( RTE ) has been recently addressed using syntactic and lexical models with some success . Here , we further explore this problem , this time using the world knowledge captured in large semantic graphs such as WordNet .
A key challenge at the core of many Natural Language Processing ( NLP ) tasks is the ability to determine which conclusions can be inferred from a given natural language text . This problem , called the Recognition of Textual Entailment ( RTE ) , has initiated the development of a range of algorithms , methods , and technologies . Unfortunately , research on Textual Entailment ( TE ) , like semantics research more generally , is fragmented into studies focussing on various aspects of semantics such as world knowledge , lexical and syntactic relations , or more specialized kinds of inference .
We proved the correctness of the new algorithm and established its efficiency analytically and empirically . The utility of our approach was illustrated on two tasks : unsupervised relation extraction from a large corpus , and the Recognizing Textual Entailment ( RTE ) benchmarks .
We introduce a new semantic annotation scheme for the Recognizing Textual Entailment ( RTE ) dataset as well as a manually annotated dataset that uses this scheme . The scheme addresses three types of modification that license entailment patterns : restrictive , appositive and conjunctive , with a formal semantic specification of these patterns ' contribution for establishing entailment .
Unlike many previous lexically-based systems , we do not measure the degree of overlap or similarity , and we do no machine learning . This simple system performs well on the Recognizing Textual Entailment ( RTE ) evaluation .
This was one of the motivations for the first Answer Validation Exercise at CLEF 2006 ( AVE 2006 ) . The starting point for the AVE 2006 was the reformulation of the answer validation as a recognizing textual entailment ( RTE ) problem , under the assumption that a hypothesis can be automatically generated instantiating a hypothesis pattern with a QA system answer . The test collections that we developed in seven different languages at AVE 2006 are specially oriented to the development and evaluation of answer validation systems .
We applied various WordNet based similarity measures to the RTE ( Recognizing Textual Entailment ) task in order to compare the effects of them on Textual Entailment Recognition . Although the improvements over a baseline system are not big , many of them show positive effects .
section : Introduction and Background Natural language inference ( NLI ) or recognizing textual entailment ( RTE ) is a fundamental semantic task in the field of natural language processing . The problem is to determine whether a given hypothesis sentence can be logically inferred from a given premise sentence .
After formal semanticists developed FraCas in the mid ’ 90 , an increase in statistical approaches to computational semantics gave rise to the need for suitable evaluation datasets . Hence , Recognizing Textual Entailment ( RTE ) shared tasks have been organized regularly . Recent work on compositional distributional models has motivated the development of the SICK dataset of sentence pairs in entailment relations for evaluating such models .
Ourmodelsupportsbatched computation for a speedup of up to 25 ◊ over other tree-structured models , and its integrated parser can operate on unparsed data with little loss in accuracy . We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence-encoding models .
Our models are trained in a regression framework , and can easily incorporate a rich set of linguistic features . The performance of our edit distance based models is contrasted with an adaptation of the Stanford textual entailment system to the STS task . Our results show that the most advanced edit distance model , pPDA , outperforms our entailment system on all but one of the genres included in the STS task .
Our model supports batched computation for a speedup of up to 25 over other tree - structured models , and its integrated parser can operate on unparsed data with little loss in accuracy . We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence - encoding models . section : Introduction
This model is a basically plausible model of human sentence processing and yields substantial accuracy gains over pure sequence - or tree - based models . We evaluate SPINN on the Stanford Natural Language Inference entailment task , and find that it significantly outperforms other sentence - encoding - based models , even with a relatively simple and underpowered implementation of the built - in parser . We also find that SPINN yields speed increases of up to 25 over a standard TreeRNN implementation .
INAOE at AVE 2007: Experiments in Spanish Answer Validation Syntactic Approximation of Semantic Roles Measuring Semantic Similarity for Bengali Tweets Using WordNet It's time for a semantic engine A Hybrid Question Answering System based on Information Retrieval and Answer Validation A Graph-based Semi-Supervised Learning for Question-Answering Design and realization of a modular architecture for textual entailment Using Sentence Semantic Similarity Based on WordNet in Recognizing Textual Entailment Combining Lexical Resources with Fuzzy Set Theory for Recognizing Textual Entailment Reasoning about Entailment with Neural Attention Order-Embeddings of Images and Language Grounded Textual Entailment Neural Tree Indexers for Text Understanding Deep contextualized word representations What Kind of Natural Language Inference are NLP Systems Learning: Is this Enough? HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks Quelo Natural Language Interface: Generating queries and answer descriptions Synergetical Use of Analytical Models and Machine-Learning for Data Transport Abstraction in Open Optical Networks Towards Recognition of Textual Entailment in the Biomedical Domain Problems In Natural-Language Interface To DSMS With Examples From EUFID Generating Persona Consistent Dialogues by Exploiting Natural Language Inference A Decomposable Attention Model for Natural Language Inference Cell-aware Stacked LSTMs for Modeling Sentences Multi-Task Deep Neural Networks for Natural Language Understanding Shortcut-Stacked Sentence Encoders for Multi-Domain Inference Attention Boosted Sequential Inference Model A large annotated corpus for learning natural language inference CAM: A Combined Attention Model for Natural Language Inference Natural Language Directed Inference in the Presentation of Ontologies Towards Improving Abstractive Summarization via Entailment Generation Natural language interference from textual entailment to conversation entailment NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference NILC at ASSIN 2: Exploring Multilingual Approaches An Investigation on Semantic Web-based Business Support: Ontology development and inference framework for the course recommendation Scaling Semantic Frame Annotation Shortcut-Stacked Sentence Encoders for Multi-Domain Inference Neural Tree Indexers for Text Understanding Neural Natural Language Inference Models Enhanced with External Knowledge A large annotated corpus for learning natural language inference Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning A Semantic Method for Textual Entailment Design and realization of a modular architecture for textual entailment Knowledge-Based Textual Inference via Parse-Tree Transformations Semantic Annotation for Textual Entailment Recognition A simple system for detecting non-entailment Testing the Reasoning for Question Answering Validation Effects of Using Simple Semantic Similarity on Textual Entailment Recognition Shortcut-Stacked Sentence Encoders for Multi-Domain Inference Grounded Textual Entailment A Fast Unified Model for Parsing and Sentence Understanding Stanford: Probabilistic Edit Distance Metrics for STS A Fast Unified Model for Parsing and Sentence Understanding
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CLEF (Working Notes) PolTAL RANLP CLEF (Notebook Papers/Labs/Workshop) ACL/IJCNLP IBERAMIA ICLR ICLR COLING EACL NAACL-HLT ICAART (2) NLDB ANLP EMNLP ACML ACL RepEval@EMNLP ArXiv EMNLP BigData ENLG NFiS@EMNLP ASSIN@STIL LAW@NAACL-HLT RepEval@EMNLP EACL ACL EMNLP ArXiv FLAIRS Conference MICAI (2) TAC TAC RepEval@EMNLP COLING ACL (1) SemEval@NAACL-HLT ACL
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