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Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment. 使用词级对齐的层次注意策略进行多模式情感分析。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2018-07-01
Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic
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引用次数: 0
Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication. 基于图标的组合语言建模的辅助和替代交流。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2018-07-01 DOI: 10.18653/v1/w18-3404
Shiran Dudy, Steven Bedrick
{"title":"Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication.","authors":"Shiran Dudy,&nbsp;Steven Bedrick","doi":"10.18653/v1/w18-3404","DOIUrl":"https://doi.org/10.18653/v1/w18-3404","url":null,"abstract":"<p><p>Icon-based communication systems are widely used in the field of Augmentative and Alternative Communication. Typically, icon-based systems have lagged behind word- and character-based systems in terms of predictive typing functionality, due to the challenges inherent to training icon-based language models. We propose a method for synthesizing training data for use in icon-based language models, and explore two different modeling strategies.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2018 ","pages":"25-32"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087438/pdf/nihms-1001619.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38939330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Training Classifiers with Natural Language Explanations. 用自然语言解释训练分类器
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2018-07-01
Braden Hancock, Martin Bringmann, Paroma Varma, Percy Liang, Stephanie Wang, Christopher Ré
{"title":"Training Classifiers with Natural Language Explanations.","authors":"Braden Hancock, Martin Bringmann, Paroma Varma, Percy Liang, Stephanie Wang, Christopher Ré","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100× faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2018 ","pages":"1884-1895"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534135/pdf/nihms-993798.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37013648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature. 一个包含患者、干预措施和结果的多层次注释的语料库，以支持医学文献的语言处理。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2018-07-01
Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain J Marshall, Ani Nenkova, Byron C Wallace
{"title":"A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature.","authors":"Benjamin Nye,&nbsp;Junyi Jessy Li,&nbsp;Roma Patel,&nbsp;Yinfei Yang,&nbsp;Iain J Marshall,&nbsp;Ani Nenkova,&nbsp;Byron C Wallace","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the 'PICO' elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2018 ","pages":"197-207"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174533/pdf/nihms-988059.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41158894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature 支持医学文献语言处理的病人、干预和结果多层次注释语料库
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2018-06-11 DOI: 10.18653/v1/P18-1019
Benjamin E. Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, I. Marshall, A. Nenkova, Byron C. Wallace
{"title":"A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature","authors":"Benjamin E. Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, I. Marshall, A. Nenkova, Byron C. Wallace","doi":"10.18653/v1/P18-1019","DOIUrl":"https://doi.org/10.18653/v1/P18-1019","url":null,"abstract":"We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the ‘PICO’ elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"120 1","pages":"197-207"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76655771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 162
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment 基于层次注意策略的多模态情感分析
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2018-05-22 DOI: 10.18653/v1/P18-1207
Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, I. Marsic
{"title":"Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment","authors":"Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, I. Marsic","doi":"10.18653/v1/P18-1207","DOIUrl":"https://doi.org/10.18653/v1/P18-1207","url":null,"abstract":"Multimodal affective computing, learning to recognize and interpret human affect and subjective information from multiple data sources, is still a challenge because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract levels, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. Our introduced model outperforms state-of-the-art approaches on published datasets, and we demonstrate that our model is able to visualize and interpret synchronized attention over modalities.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"68 1","pages":"2225-2235"},"PeriodicalIF":0.0,"publicationDate":"2018-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79113531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 100
Training Classifiers with Natural Language Explanations 用自然语言解释训练分类器
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2018-05-10 DOI: 10.18653/v1/P18-1175
Braden Hancock, P. Varma, Stephanie Wang, Martin Bringmann, Percy Liang, C. Ré
{"title":"Training Classifiers with Natural Language Explanations","authors":"Braden Hancock, P. Varma, Stephanie Wang, Martin Bringmann, Percy Liang, C. Ré","doi":"10.18653/v1/P18-1175","DOIUrl":"https://doi.org/10.18653/v1/P18-1175","url":null,"abstract":"Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100 faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2 1","pages":"1884-1895"},"PeriodicalIF":0.0,"publicationDate":"2018-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83375273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 140
Automating Biomedical Evidence Synthesis: RobotReviewer. 自动化生物医学证据合成:RobotReviewer。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2017-07-01 DOI: 10.18653/v1/P17-4002
Iain J Marshall, Joël Kuiper, Edward Banner, Byron C Wallace
{"title":"Automating Biomedical Evidence Synthesis: RobotReviewer.","authors":"Iain J Marshall, Joël Kuiper, Edward Banner, Byron C Wallace","doi":"10.18653/v1/P17-4002","DOIUrl":"10.18653/v1/P17-4002","url":null,"abstract":"<p><p>We present <i>RobotReviewer</i>, an open-source web-based system that uses machine learning and NLP to semi-automate biomedical evidence synthesis, to aid the practice of Evidence-Based Medicine. RobotReviewer processes full-text journal articles (PDFs) describing randomized controlled trials (RCTs). It appraises the reliability of RCTs and extracts text describing key trial characteristics (e.g., descriptions of the population) using novel NLP methods. RobotReviewer then automatically generates a report synthesising this information. Our goal is for RobotReviewer to automatically extract and synthesise the full-range of structured data needed to inform evidence-based practice.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2017 ","pages":"7-12"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662138/pdf/nihms880383.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35568129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 54
Neural Semantic Encoders. 神经语义编码器。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2017-04-01
Tsendsuren Munkhdalai, Hong Yu
{"title":"Neural Semantic Encoders.","authors":"Tsendsuren Munkhdalai,&nbsp;Hong Yu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through <i>read</i>, <i>compose</i> and <i>write</i> operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"1 ","pages":"397-407"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5657452/pdf/nihms912000.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35553901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Neural Tree Indexers for Text Understanding. 文本理解的神经树索引器。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2017-04-01
Tsendsuren Munkhdalai, Hong Yu
{"title":"Neural Tree Indexers for Text Understanding.","authors":"Tsendsuren Munkhdalai,&nbsp;Hong Yu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic tree-based recursive models. NTI constructs a <i>full n-ary tree</i> by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and node function. We implemented and evaluated a binary-tree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification, outperforming state-of-the-art recurrent and recursive neural networks .</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"1 ","pages":"11-21"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5657441/pdf/nihms912010.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35552522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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