Bi-LMs are estimated by first creating bitoken sequences using a parallel corpus and the word alignments between the source and target words in that corpus . When creating the bitoken sequences , the vocabulary expands considerably and Bi-LMs suffer due to this huge vocabulary which in turn increases the sparsity of the language models . In previous work , bitokens were created by first replacing each word in the parallel corpus either by their part-of-speech tags or word classes after clustering using the Brown clustering algorithm . Finally , the dissertation develops static analyses that help optimize monolithic MPC programs into mixed-mode MPC programs , while providing similar privacy guarantees as the monolithic versions . Language-based Techniques for Practical and Trustworthy Secure Multi-party Computations The Challenge of Hardware-Software Co-verification.- From the How to the What.- An Overview of Separation Logic.- A Perspective on Program Verification.- Meta-Logical Frameworks and Formal Digital Libraries.- Languages , Ambiguity , and Verification.- The Importance of Non-theorems and Counterexamples in Program Verification.- Regression Verification - A Practical Way to Verify Programs.- Programming with Proofs : Language-Based Approaches to Totally Correct Software.- The Role of Model-Based Testing.- Abstraction of Graph Transformation Systems by Temporal Logic and Its Verification.- Program Verification by Using DISCOVERER.- Constraint Solving and Symbolic Execution . Coherence of a text is provided by various language means , including discourse connectives ( coordinating and subordinating conjunctions , adverbs etc . ) . However , semantic relations between text segments can be deduced without an explicit discourse connective , too ( the so called implicit discourse relations , cf . The article is devoted to studying the ways of the realization of the concepts of time and space in the language of F. I. Tyutchev 's poetry . Special attention in the article is given to the analysis of the language means used for the creation of the model of art space . Thus the functional potential of verbal symbols of time is revealed . No well-specified approach exists . As a foundation for applying language-level techniques like program sketching and synthesis to this problem , we present a formal model for interrupt routing which can capture all the system topologies and interrupt controllers we have encountered in the wild , show applications of such a model not possible with informal , ad-hoc approaches like DeviceTrees , and finally discuss an implementation based on the model which forms the new interrupt subsystem of the Barrelfish OS . Mental health is a significant and growing public health concern . As language usage can be leveraged to obtain crucial insights into mental health conditions , there is a need for large-scale , labeled , mental health-related datasets of users who have been diagnosed with one or more of such conditions . In this paper , we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions , and obtain high-quality labeled data without the need for manual labelling . Feasibility of learning interactive multimedia of integrated science themed foods containing formaldehyde based on the results of the expert validation of multimedia , science teachers assessment results and students responses . Multimedia expert validation include display design , language use , voting , and navigation buttons . Teachers assessment includes multimedia display , language clarity , legibility of text , harmony of color , clarity narrator , suitability musical accompaniment , and the ease of use of multimedia . In interactive software development , though , theory and practice have placed little emphasis on the role and importance of this crucial aspect of cognition in user interactions , with the result that user interface and interaction designs are typically impoverished in ways they need not be . In this paper , the basic features of a language use approach to human-computer interaction are outlined , and a range of both noncomputational and computational implications for the design of interactive systems is examined . In particular , human-computer interaction is recast as a genuine instance of language use between the user and the system designer and , in a second layer , as a joint activity in which the system and the user are participants . The sophisticated use of natural language requires reasoning about other agents , what they might do and what they believe , and therefore provides a suitable domain for planning to achieve goals involving belief . This paper does not directly address issues of language per se , but focuses on the problem-solving requirements of a language-using system , and describes a working system , kamp ( Knowledge And Modalities Planner ) , that embodies the ideas reported herein . This article probes into the effectiveness which the language environment has exerted on the language expression from two sides : the meaning and the function ( language environment to expression effect ) , identifies that the successes as well as the failures in language using depend on the language environment . Furthermore , it points out that we pay less attention to it in our traditional language teaching , and theoretically put forward an ideal model for language teaching . A proposed language that incorporates these requirements is then described . The proposed language Language for Concurrent Simulation has some of the key characteristics of Small talk-SO and the Lisp Flavor System . The language is unique , to the author 1 s knowledge , in providing both classand instance-oriented inheritance of object behaviors in a compiled language . The subjects of this research are 6 members of the language section in Darul Istiqamah Boarding School . The objects of this research are the language section strategies in improving students ’ speaking at the tenth grade of Darul Istiqamah Boarding School for boys and the difficulties that language section finds in improving students ’ speaking . To collect the data , the writer uses the techniques of observation , interview , and documentary . Data processing in this research is divided into three phases : editing , classification , and data interpretation . Then , all the data are analyzed descriptively and qualitatively . The language section ’s strategies in improving tenth grade students’speaking at Darul Istiqamah for boy are controlling and giving vocabulary , conducting the test , giving punishment , language area , watcher , scheduled conversation , speech presentation , drama , special English class , and scouting with English . the difficulties that are found by language section are difficult to control students 24 hours , lack of model and from a certain section in OPPM , and lack of awareness importance in using language . Such an approach has many advantages . For instance , thelanguage models obtained can be easily adapted into previously existing taggingformalisms ; the two modules can be improved and extended separately ; etc . As a first step , we have experimentally proven that decision trees ( DT ) providea flexible ( by allowing a rich feature representation ) , efficient and compact wayfor acquiring , representing and accessing the information about POS ambiguities . LMs have played a key role in traditional NLP tasks such as speech recognition , machine translation , or text summarization . Often ( although not always ) , training better language models improves the underlying metrics of the downstream task ( such as word error rate for speech recognition , or BLEU score for translation ) , which makes the task of training better LMs valuable by itself . Further , when trained on vast amounts of data , language models compactly extract knowledge encoded in the training data . The SAT system predicts an overall score for a given text , which provides a holistic assessment of linguistic competence and language proficiency . The authors trained a supervised ranking perceptron model on the FCE - public dataset , using features such as error - rate estimates from a language model and various lexical and grammatical properties of text ( e.g. , word n - grams , part - of - speech n - grams and phrase - structure rules ) . We replicate this experiment and add the average probability of each token in the essay being correct , according to the error detection model , as an additional feature for the scoring framework . section : Introduction User issued search queries often do not follow formal grammatical structure , and require specialized language processing . Traditional natural language processing ( NLP ) tools trained on formal text ( e.g. treebanks ) often have difficulty analyzing search queries ; the lack of regularity in the structure of queries makes it difficult to train models that can optimally process the query to extract information that can help understand the user intent behind the query . Adding to this challenge , most methods for VQA process the question through a recurrent neural network ( such as an LSTM ) trained from scratch solely on the training questions . paragraph : Language representation The above reasons motivate us to take advantage of the extensive existing work in the natural language community to aid processing the questions . Universal Language Model for Fine-tuning [ arXiv:1801.06146 ] ( ULMFiT ) is one of the first NLP methods for efficient inductive transfer learning . Unsupervised pretraining results in improvements on many NLP tasks for English . Due to the linguistically noisy and domain-specific nature of the content , our unique data preprocessing steps designed for Thai social media were utilized to ease the training comprehension of the model . We compared four modern language models : ULMFiT , ELMo with biLSTM , OpenAI GPT , and BERT . We systematically compared the models across different dimensions including speed of pretraining and fine-tuning , perplexity , downstream classification benchmarks , and performance in limited pretraining data . Inductive transfer learning has greatly impacted computer vision , but existing approaches in NLP still require task-specific modifications and training from scratch . We propose Universal Language Model Fine-tuning ( ULMFiT ) , an effective transfer learning method that can be applied to any task in NLP , and introduce techniques that are key for fine-tuning a language model . Our method significantly outperforms the state-of-the-art on six text classification tasks , reducing the error by 18 - 24 % on the majority of datasets . More researches have shown that real-time Twitter data can be used to predict market movement of securities and other financial instruments . The Universal Language Model Fine-Tuning ( ULMFiT ) is a new approach which is based on training a language model and transferring its knowledge to a final classifier . We propose to fine tune the ULMFiT model by optimizing the parameters and training the model in a deterministic approach to increase the reproducibility . The most common transfer learning method in NLP is the use of pre-trained word embeddings . The Universal Language Model Fine-Tuning ( ULMFiT ) is a recent approach which proposes to train a language model and transfer its knowledge to a final classifier . During the classification step , ULMFiT uses a max and average pooling layer to select the useful information of an embedding sequence . This research paper explores the application of the Universal Language Model Fine-tuning ( ULMFiT ) technique , a novel deep transfer learning approach in the Natural Language Processing ( NLP ) field , to the financial statements fraud detection task . Additionally , the artifact investigated a simpler model , represented by a one-dimensional Convolutional Neural Network ( CNN ) . Pre-trained language models have opened up new possibilities for classification tasks with limited labelled data , because representation can be learned in an unsupervised fashion . In our experiments we have studied the effects of training set size ( 100 - 1600 items ) on the prediction accuracy of a ULMFiT classifier , based on a language models that we pre-trained on the Dutch Wikipedia . We also compared ULMFiT to Support Vector Machines , which is traditionally considered suitable for small collections . Deep neural networks have been employed to detect hate speech from online content . This paper describes three different Deep Neural Network ( DNN ) Architectures for detection of hate words in Twitter - Gated Recurrent Unit ( GRU ) , useful in capturing sequence orders , Convolution Neural Network ( CNN ) , good for feature extraction , and Universal Language Model Fine-tuning ( ULMFiT ) model , which is based on transfer learning technique . ULMFiT model uses the DNN Architecture called Average-SGD Weight-Dropped Long Short Term Memory ( AWD-LSTM ) . In this paper , we aim at developing an algorithm for Spanish Sentiment Analysis of short texts that is comparable to the state-of-the-art . In order to do so , we have adapted the ULMFiT algorithm to this setting . Experimental results on benchmark datasets ( InterTASS 2017 and InterTASS 2018 ) show how this simple transfer learning approach performs well when compared to fancy deep learning techniques . Then new words from problem ’s domain are matched to the tokens accordingly obtained hierarchy . In the experimental part the proposed approach is demonstrated on the slightly modified ULMFiT language model . This paper presents our contribution to PolEval 2019 Task 6 : Hate speech and bullying detection . We describe three parallel approaches that we followed : fine-tuning a pre-trained ULMFiT model to our classification task , fine-tuning a pre-trained BERT model to our classification task , and using the TPOT library to find the optimal pipeline . We present results achieved by these three tools and review their advantages and disadvantages in terms of user experience . We contend that the lack of specificity in generic sentence classification models may not be the best way to tackle text subtleties that intrinsically prevail in a classification task as complex as identifying disclosures of sexual harassment . We propose the Disclosure Language Model , a three-part ULMFiT architecture , consisting of a Language model , a Medium-Specific ( Twitter ) model , and a Task-Specific classifier to tackle this problem and create a manually annotated real-world dataset to test our technique on this , to show that using a Discourse Language Model often yields better classification performance over ( i ) Generic deep learning based sentence classification models ( ii ) existing models that rely on handcrafted stylistic features . An extensive comparison with stateof-the-art generic and specific models along with a detailed error analysis presents the case for our proposed methodology . A small saturable transformer is used to supplythe transistor drive requirements in the improved squarewave oscillator circuit . The feedback transformer derives its primary power for sufficient positive feedback from the output circuit either as a voltagedependent signal , a current-dependent signal , or a combination of both . Mter a period of time , the feedback transformer core saturates and the impedance of the feedback circuit causes a reduction in feedback power . It decides when and how to evolve by evaluating the impact of the evolution in the precedent feedback cycle . These strategies are implemented in a feedback transformer component and an adaptation manager component respectively , constituting a new adaptation layer . The adaptive ontology is evaluated with an experiment and validated with a real-world conversational content-based e-commerce recommender system as use case . A 60-GHz dual-mode power amplifier ( PA ) is implemented in 40-nm bulk CMOS technology . To boost the amplifier performance at millimeter-wave ( mmWave ) frequencies , a new transistor layout is proposed to minimize the device and interconnect parasitics while the neutralized amplifier stage is co-optimized with input transformer to improve the power gain and stability . The transformer-based power-combining PA consists of two unit amplifiers , operating in Class AB for better back-off efficiency . Targeting at both high efficiency and performance , we propose AlignTTS to predict the mel-spectrum in parallel . AlignTTS is based on a Feed-Forward Transformer which generates mel-spectrum from a sequence of characters , and the duration of each character is determined by a duration predictor . Instead of adopting the attention mechanism in Transformer TTS to align text to mel-spectrum , the alignment loss is presented to consider all possible alignments in training by use of dynamic programming . Inductive transfer learning has greatly impacted computer vision , but existing approaches in NLP still require task-specific modifications and training from scratch . We propose Universal Language Model Fine-tuning ( ULMFiT ) , an effective transfer learning method that can be applied to any task in NLP , and introduce techniques that are key for fine-tuning a language model . Our method significantly outperforms the state-of-the-art on six text classification tasks , reducing the error by 18 - 24 % on the majority of datasets . Manual annotation of about 2,000 items proved the reliability of the corpus construction approach . Further , we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 on test set , which constitutes a considerable gain over baseline methods . The dataset is freely available for research community . Manual annotation of 2,000 items proved the reliability of corpus construction approach . Further , we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 , which constitutes a considerable gain over baseline methods . The objective of this study was to determine whether modern deep-learning language-modeling techniques could reliably match pathology reports to pertinent radiology reports . METHODS The recently proposed Universal Language Model Fine-Tuning for Text Classification methodology was used . Two hundred thousand radiology and pathology reports were used for adaptation to the radiology-pathology space . Efficient Multi-lingual Language Model Fine-tuning . Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data . However , training existing models requires huge amounts of compute , while pretrained cross-lingual models often underperform on low-resource languages . We propose Multi-lingual language model Fine-Tuning ( MultiFiT ) to enable practitioners to train and fine-tune language models efficiently in their own language . In addition , we propose a zero-shot method using an existing pretrained cross-lingual model . In this paper , we present our participation in the Bacteria Biotope ( BB ) task at BioNLP-OST 2019 . Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition , normalization and relation extraction . It achieves the state-ofthe-art performance and is among the top two systems in five of all six subtasks . The second stage is a language model fine-tuning task . In a word , stage one use graph neural network to select and concatenate support sentences as one paragraph , and stage two find the answer span in language model fine-tuning paradigm .
Bi-LMs are estimated by first creating bitoken sequences using a parallel corpus and the word alignments between the source and target words in that corpus . When creating the bitoken sequences , the vocabulary expands considerably and Bi-LMs suffer due to this huge vocabulary which in turn increases the sparsity of the language models . In previous work , bitokens were created by first replacing each word in the parallel corpus either by their part-of-speech tags or word classes after clustering using the Brown clustering algorithm .
Finally , the dissertation develops static analyses that help optimize monolithic MPC programs into mixed-mode MPC programs , while providing similar privacy guarantees as the monolithic versions . Language-based Techniques for Practical and Trustworthy Secure Multi-party Computations
The Challenge of Hardware-Software Co-verification.- From the How to the What.- An Overview of Separation Logic.- A Perspective on Program Verification.- Meta-Logical Frameworks and Formal Digital Libraries.- Languages , Ambiguity , and Verification.- The Importance of Non-theorems and Counterexamples in Program Verification.- Regression Verification - A Practical Way to Verify Programs.- Programming with Proofs : Language-Based Approaches to Totally Correct Software.- The Role of Model-Based Testing.- Abstraction of Graph Transformation Systems by Temporal Logic and Its Verification.- Program Verification by Using DISCOVERER.- Constraint Solving and Symbolic Execution .
Coherence of a text is provided by various language means , including discourse connectives ( coordinating and subordinating conjunctions , adverbs etc . ) . However , semantic relations between text segments can be deduced without an explicit discourse connective , too ( the so called implicit discourse relations , cf .
The article is devoted to studying the ways of the realization of the concepts of time and space in the language of F. I. Tyutchev 's poetry . Special attention in the article is given to the analysis of the language means used for the creation of the model of art space . Thus the functional potential of verbal symbols of time is revealed .
No well-specified approach exists . As a foundation for applying language-level techniques like program sketching and synthesis to this problem , we present a formal model for interrupt routing which can capture all the system topologies and interrupt controllers we have encountered in the wild , show applications of such a model not possible with informal , ad-hoc approaches like DeviceTrees , and finally discuss an implementation based on the model which forms the new interrupt subsystem of the Barrelfish OS .
Mental health is a significant and growing public health concern . As language usage can be leveraged to obtain crucial insights into mental health conditions , there is a need for large-scale , labeled , mental health-related datasets of users who have been diagnosed with one or more of such conditions . In this paper , we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions , and obtain high-quality labeled data without the need for manual labelling .
Feasibility of learning interactive multimedia of integrated science themed foods containing formaldehyde based on the results of the expert validation of multimedia , science teachers assessment results and students responses . Multimedia expert validation include display design , language use , voting , and navigation buttons . Teachers assessment includes multimedia display , language clarity , legibility of text , harmony of color , clarity narrator , suitability musical accompaniment , and the ease of use of multimedia .
In interactive software development , though , theory and practice have placed little emphasis on the role and importance of this crucial aspect of cognition in user interactions , with the result that user interface and interaction designs are typically impoverished in ways they need not be . In this paper , the basic features of a language use approach to human-computer interaction are outlined , and a range of both noncomputational and computational implications for the design of interactive systems is examined . In particular , human-computer interaction is recast as a genuine instance of language use between the user and the system designer and , in a second layer , as a joint activity in which the system and the user are participants .
The sophisticated use of natural language requires reasoning about other agents , what they might do and what they believe , and therefore provides a suitable domain for planning to achieve goals involving belief . This paper does not directly address issues of language per se , but focuses on the problem-solving requirements of a language-using system , and describes a working system , kamp ( Knowledge And Modalities Planner ) , that embodies the ideas reported herein .
This article probes into the effectiveness which the language environment has exerted on the language expression from two sides : the meaning and the function ( language environment to expression effect ) , identifies that the successes as well as the failures in language using depend on the language environment . Furthermore , it points out that we pay less attention to it in our traditional language teaching , and theoretically put forward an ideal model for language teaching .
A proposed language that incorporates these requirements is then described . The proposed language Language for Concurrent Simulation has some of the key characteristics of Small talk-SO and the Lisp Flavor System . The language is unique , to the author 1 s knowledge , in providing both classand instance-oriented inheritance of object behaviors in a compiled language .
The subjects of this research are 6 members of the language section in Darul Istiqamah Boarding School . The objects of this research are the language section strategies in improving students ’ speaking at the tenth grade of Darul Istiqamah Boarding School for boys and the difficulties that language section finds in improving students ’ speaking . To collect the data , the writer uses the techniques of observation , interview , and documentary . Data processing in this research is divided into three phases : editing , classification , and data interpretation . Then , all the data are analyzed descriptively and qualitatively . The language section ’s strategies in improving tenth grade students’speaking at Darul Istiqamah for boy are controlling and giving vocabulary , conducting the test , giving punishment , language area , watcher , scheduled conversation , speech presentation , drama , special English class , and scouting with English . the difficulties that are found by language section are difficult to control students 24 hours , lack of model and from a certain section in OPPM , and lack of awareness importance in using language .
Such an approach has many advantages . For instance , thelanguage models obtained can be easily adapted into previously existing taggingformalisms ; the two modules can be improved and extended separately ; etc . As a first step , we have experimentally proven that decision trees ( DT ) providea flexible ( by allowing a rich feature representation ) , efficient and compact wayfor acquiring , representing and accessing the information about POS ambiguities .
LMs have played a key role in traditional NLP tasks such as speech recognition , machine translation , or text summarization . Often ( although not always ) , training better language models improves the underlying metrics of the downstream task ( such as word error rate for speech recognition , or BLEU score for translation ) , which makes the task of training better LMs valuable by itself . Further , when trained on vast amounts of data , language models compactly extract knowledge encoded in the training data .
The SAT system predicts an overall score for a given text , which provides a holistic assessment of linguistic competence and language proficiency . The authors trained a supervised ranking perceptron model on the FCE - public dataset , using features such as error - rate estimates from a language model and various lexical and grammatical properties of text ( e.g. , word n - grams , part - of - speech n - grams and phrase - structure rules ) . We replicate this experiment and add the average probability of each token in the essay being correct , according to the error detection model , as an additional feature for the scoring framework .
section : Introduction User issued search queries often do not follow formal grammatical structure , and require specialized language processing . Traditional natural language processing ( NLP ) tools trained on formal text ( e.g. treebanks ) often have difficulty analyzing search queries ; the lack of regularity in the structure of queries makes it difficult to train models that can optimally process the query to extract information that can help understand the user intent behind the query .
Adding to this challenge , most methods for VQA process the question through a recurrent neural network ( such as an LSTM ) trained from scratch solely on the training questions . paragraph : Language representation The above reasons motivate us to take advantage of the extensive existing work in the natural language community to aid processing the questions .
Universal Language Model for Fine-tuning [ arXiv:1801.06146 ] ( ULMFiT ) is one of the first NLP methods for efficient inductive transfer learning . Unsupervised pretraining results in improvements on many NLP tasks for English .
Due to the linguistically noisy and domain-specific nature of the content , our unique data preprocessing steps designed for Thai social media were utilized to ease the training comprehension of the model . We compared four modern language models : ULMFiT , ELMo with biLSTM , OpenAI GPT , and BERT . We systematically compared the models across different dimensions including speed of pretraining and fine-tuning , perplexity , downstream classification benchmarks , and performance in limited pretraining data .
Inductive transfer learning has greatly impacted computer vision , but existing approaches in NLP still require task-specific modifications and training from scratch . We propose Universal Language Model Fine-tuning ( ULMFiT ) , an effective transfer learning method that can be applied to any task in NLP , and introduce techniques that are key for fine-tuning a language model . Our method significantly outperforms the state-of-the-art on six text classification tasks , reducing the error by 18 - 24 % on the majority of datasets .
More researches have shown that real-time Twitter data can be used to predict market movement of securities and other financial instruments . The Universal Language Model Fine-Tuning ( ULMFiT ) is a new approach which is based on training a language model and transferring its knowledge to a final classifier . We propose to fine tune the ULMFiT model by optimizing the parameters and training the model in a deterministic approach to increase the reproducibility .
The most common transfer learning method in NLP is the use of pre-trained word embeddings . The Universal Language Model Fine-Tuning ( ULMFiT ) is a recent approach which proposes to train a language model and transfer its knowledge to a final classifier . During the classification step , ULMFiT uses a max and average pooling layer to select the useful information of an embedding sequence .
This research paper explores the application of the Universal Language Model Fine-tuning ( ULMFiT ) technique , a novel deep transfer learning approach in the Natural Language Processing ( NLP ) field , to the financial statements fraud detection task . Additionally , the artifact investigated a simpler model , represented by a one-dimensional Convolutional Neural Network ( CNN ) .
Pre-trained language models have opened up new possibilities for classification tasks with limited labelled data , because representation can be learned in an unsupervised fashion . In our experiments we have studied the effects of training set size ( 100 - 1600 items ) on the prediction accuracy of a ULMFiT classifier , based on a language models that we pre-trained on the Dutch Wikipedia . We also compared ULMFiT to Support Vector Machines , which is traditionally considered suitable for small collections .
Deep neural networks have been employed to detect hate speech from online content . This paper describes three different Deep Neural Network ( DNN ) Architectures for detection of hate words in Twitter - Gated Recurrent Unit ( GRU ) , useful in capturing sequence orders , Convolution Neural Network ( CNN ) , good for feature extraction , and Universal Language Model Fine-tuning ( ULMFiT ) model , which is based on transfer learning technique . ULMFiT model uses the DNN Architecture called Average-SGD Weight-Dropped Long Short Term Memory ( AWD-LSTM ) .
In this paper , we aim at developing an algorithm for Spanish Sentiment Analysis of short texts that is comparable to the state-of-the-art . In order to do so , we have adapted the ULMFiT algorithm to this setting . Experimental results on benchmark datasets ( InterTASS 2017 and InterTASS 2018 ) show how this simple transfer learning approach performs well when compared to fancy deep learning techniques .
Then new words from problem ’s domain are matched to the tokens accordingly obtained hierarchy . In the experimental part the proposed approach is demonstrated on the slightly modified ULMFiT language model .
This paper presents our contribution to PolEval 2019 Task 6 : Hate speech and bullying detection . We describe three parallel approaches that we followed : fine-tuning a pre-trained ULMFiT model to our classification task , fine-tuning a pre-trained BERT model to our classification task , and using the TPOT library to find the optimal pipeline . We present results achieved by these three tools and review their advantages and disadvantages in terms of user experience .
We contend that the lack of specificity in generic sentence classification models may not be the best way to tackle text subtleties that intrinsically prevail in a classification task as complex as identifying disclosures of sexual harassment . We propose the Disclosure Language Model , a three-part ULMFiT architecture , consisting of a Language model , a Medium-Specific ( Twitter ) model , and a Task-Specific classifier to tackle this problem and create a manually annotated real-world dataset to test our technique on this , to show that using a Discourse Language Model often yields better classification performance over ( i ) Generic deep learning based sentence classification models ( ii ) existing models that rely on handcrafted stylistic features . An extensive comparison with stateof-the-art generic and specific models along with a detailed error analysis presents the case for our proposed methodology .
A small saturable transformer is used to supplythe transistor drive requirements in the improved squarewave oscillator circuit . The feedback transformer derives its primary power for sufficient positive feedback from the output circuit either as a voltagedependent signal , a current-dependent signal , or a combination of both . Mter a period of time , the feedback transformer core saturates and the impedance of the feedback circuit causes a reduction in feedback power .
It decides when and how to evolve by evaluating the impact of the evolution in the precedent feedback cycle . These strategies are implemented in a feedback transformer component and an adaptation manager component respectively , constituting a new adaptation layer . The adaptive ontology is evaluated with an experiment and validated with a real-world conversational content-based e-commerce recommender system as use case .
A 60-GHz dual-mode power amplifier ( PA ) is implemented in 40-nm bulk CMOS technology . To boost the amplifier performance at millimeter-wave ( mmWave ) frequencies , a new transistor layout is proposed to minimize the device and interconnect parasitics while the neutralized amplifier stage is co-optimized with input transformer to improve the power gain and stability . The transformer-based power-combining PA consists of two unit amplifiers , operating in Class AB for better back-off efficiency .
Targeting at both high efficiency and performance , we propose AlignTTS to predict the mel-spectrum in parallel . AlignTTS is based on a Feed-Forward Transformer which generates mel-spectrum from a sequence of characters , and the duration of each character is determined by a duration predictor . Instead of adopting the attention mechanism in Transformer TTS to align text to mel-spectrum , the alignment loss is presented to consider all possible alignments in training by use of dynamic programming .
Inductive transfer learning has greatly impacted computer vision , but existing approaches in NLP still require task-specific modifications and training from scratch . We propose Universal Language Model Fine-tuning ( ULMFiT ) , an effective transfer learning method that can be applied to any task in NLP , and introduce techniques that are key for fine-tuning a language model . Our method significantly outperforms the state-of-the-art on six text classification tasks , reducing the error by 18 - 24 % on the majority of datasets .
Manual annotation of about 2,000 items proved the reliability of the corpus construction approach . Further , we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 on test set , which constitutes a considerable gain over baseline methods . The dataset is freely available for research community .
Manual annotation of 2,000 items proved the reliability of corpus construction approach . Further , we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 , which constitutes a considerable gain over baseline methods .
The objective of this study was to determine whether modern deep-learning language-modeling techniques could reliably match pathology reports to pertinent radiology reports . METHODS The recently proposed Universal Language Model Fine-Tuning for Text Classification methodology was used . Two hundred thousand radiology and pathology reports were used for adaptation to the radiology-pathology space .
Efficient Multi-lingual Language Model Fine-tuning . Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data . However , training existing models requires huge amounts of compute , while pretrained cross-lingual models often underperform on low-resource languages . We propose Multi-lingual language model Fine-Tuning ( MultiFiT ) to enable practitioners to train and fine-tune language models efficiently in their own language . In addition , we propose a zero-shot method using an existing pretrained cross-lingual model .
In this paper , we present our participation in the Bacteria Biotope ( BB ) task at BioNLP-OST 2019 . Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition , normalization and relation extraction . It achieves the state-ofthe-art performance and is among the top two systems in five of all six subtasks .
The second stage is a language model fine-tuning task . In a word , stage one use graph neural network to select and concatenate support sentences as one paragraph , and stage two find the answer span in language model fine-tuning paradigm .
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TSD PLOS@SOSP COLING AAAI ArXiv ACL EMNLP 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ACIIDS (1) IJCNN MICAI EUSFLAT Conf. NAACL-HLT (Student Research Workshop) ICASSP ACL (1) WWW (Companion Volume) BioNLP-OST@EMNLP-IJNCLP
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