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{
 "paper_id": "S19-2000",
 "header": {
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 "date_generated": "2023-01-19T15:47:37.144130Z"
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 "title": "Invited Talk: Task-Independent Sentence Understanding",
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 "abstract": "This talk deals with the goal of task-independent language understanding: building machine learning models that can learn to do most of the hard work of language understanding before they see a single example of the language understanding task they're meant to solve, in service of making the best of modern NLP systems both better and more data-efficient. I'll survey the (dramatic!) progress that the NLP research community has made toward this goal in the last year. In particular, I'll dwell on GLUE-an open-ended shared task competition that measures progress toward this goal for sentence understanding tasks-and I'll preview a few recent and forthcoming analysis papers that attempt to offer a bit of perspective on this recent progress. Biography I have been on the faculty at NYU since 2016, when I finished my PhD with Chris Manning and Chris Potts at Stanford. At NYU, I'm a core member of the new school-level Data Science unit, which focuses on machine learning, and a co-PI of the CILVR machine learning lab. My research focuses on data, evaluation techniques, and modeling techniques for sentence understanding in natural language processing, and on applications of machine learning to scientific questions in linguistic syntax and semantics. I am an area chair for *",
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 "text": "This talk deals with the goal of task-independent language understanding: building machine learning models that can learn to do most of the hard work of language understanding before they see a single example of the language understanding task they're meant to solve, in service of making the best of modern NLP systems both better and more data-efficient. I'll survey the (dramatic!) progress that the NLP research community has made toward this goal in the last year. In particular, I'll dwell on GLUE-an open-ended shared task competition that measures progress toward this goal for sentence understanding tasks-and I'll preview a few recent and forthcoming analysis papers that attempt to offer a bit of perspective on this recent progress. Biography I have been on the faculty at NYU since 2016, when I finished my PhD with Chris Manning and Chris Potts at Stanford. At NYU, I'm a core member of the new school-level Data Science unit, which focuses on machine learning, and a co-PI of the CILVR machine learning lab. My research focuses on data, evaluation techniques, and modeling techniques for sentence understanding in natural language processing, and on applications of machine learning to scientific questions in linguistic syntax and semantics. I am an area chair for *",
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 "text": "Welcome to SemEval-2019! The Semantic Evaluation (SemEval) series of workshops focuses on the evaluation and comparison of systems that can analyse diverse semantic phenomena in text with the aim of extending the current state of the art in semantic analysis and creating high quality annotated datasets in a range of increasingly challenging problems in natural language semantics. SemEval provides an exciting forum for researchers to propose challenging research problems in semantics and to build systems/techniques to address such research problems.",
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 "section": "Introduction",
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 "text": "SemEval-2019 is the thirteenth workshop in the series of International Workshops on Semantic Evaluation. The first three workshops, SensEval-1 (1998), SensEval-2 (2001) , and SensEval-3 (2004) , focused on word sense disambiguation, each time growing in the number of languages offered, in the number of tasks, and also in the number of participating teams. In 2007, the workshop was renamed to SemEval, and the subsequent SemEval workshops evolved to include semantic analysis tasks beyond word sense disambiguation. In 2012, SemEval turned into a yearly event. It currently runs every year, but on a two-year cycle, i.e., the tasks for SemEval 2019 were proposed in 2018. This volume contains both Task Description papers that describe each of the above tasks, and System Description papers that present the systems that participated in these tasks. A total of 11 task description papers and 220 system description papers are included in this volume.",
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 "section": "Introduction",
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 "text": "We are grateful to all task organizers as well as to the large number of participants whose enthusiastic participation has made SemEval once again a successful event. We are thankful to the task organizers who also served as area chairs, and to task organizers and participants who reviewed paper submissions. These proceedings have greatly benefited from their detailed and thoughtful feedback. We also thank the NAACL HLT 2019 conference organizers for their support. Finally, we most gratefully acknowledge the support of our sponsors: the ACL Special Interest Group on the Lexicon (SIGLEX) and Microsoft.",
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 "section": "Introduction",
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 "text": "CX-ST- RNM at SemEval-2019 ",
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 "section": "acknowledgement",
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 "year": 2018,
 "venue": "Poster Session Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos and Yiannis Kompatsiaris Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection Carla Perez Almendros",
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 "raw_text": "Friday, June 7, 2019 (continued) SemEval-2019 Task 9: Suggestion Mining from Online Reviews and Forums Sapna Negi, Tobias Daudert and Paul Buitelaar m_y at SemEval-2019 Task 9: Exploring BERT for Suggestion Mining Masahiro Yamamoto and Toshiyuki Sekiya SemEval-2019 Task 10: Math Question Answering Mark Hopkins, Ronan Le Bras, Cristian Petrescu-Prahova, Gabriel Stanovsky, Han- naneh Hajishirzi and Rik Koncel-Kedziorski AiFu at SemEval-2019 Task 10: A Symbolic and Sub-symbolic Integrated System for SAT Math Question Answering Yifan Liu, Keyu Ding and Yi Zhou SemEval-2019 Task 12: Toponym Resolution in Scientific Papers Davy Weissenbacher, Arjun Magge, Karen O'Connor, Matthew Scotch and Graciela Gonzalez-Hernandez DM_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution Xiaobin Wang, Chunping Ma, Huafei Zheng, Chu Liu, Pengjun Xie, Linlin Li and Luo Si 15:30-16:00 Coffee 16:00-16:30 Discussion 16:30-17:30 Poster Session Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Pa- padopoulos and Yiannis Kompatsiaris Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection Carla Perez Almendros, Luis Espinosa Anke and Steven Schockaert Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection Viresh Gupta, Baani Leen Kaur Jolly, Ramneek Kaur and Tanmoy Chakraborty xxxix",
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 "BIBREF138": {
 "ref_id": "b138",
 "title": "Hyperpartisan News Detection with Generic Semi-supervised Features Rodrigo Agerri Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector Saptarshi Sengupta and Ted Pedersen Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector Nikhil Chakravartula, Vijayasaradhi Indurthi and Bakhtiyar Syed Harvey Mudd College at SemEval-2019 Task 4: The Carl Kolchak Hyperpartisan News Detector Celena Chen, Celine Park, Jason Dwyer and Julie Medero Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector Mehdi Drissi, Pedro Sandoval Segura, Vivaswat Ojha and Julie Medero Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector Evan Amason, Jake Palanker, Mary Clare Shen and Julie Medero NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector Duc-Vu Nguyen, Thin Dang and Ngan Nguyen Orwellian-times at SemEval-2019 Task 4: A Stylistic and Content-based Classifier J\u00fcrgen Knauth Rouletabille at SemEval-2019 Task 4: Neural Network Baseline for Identification of",
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 "venue": "SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection Tim Isbister and Fredrik Johansson Doris Martin at SemEval-2019 Task",
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 "raw_text": "Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyper- partisan News Detection Tim Isbister and Fredrik Johansson Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features Rodrigo Agerri Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector Saptarshi Sengupta and Ted Pedersen Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector Nikhil Chakravartula, Vijayasaradhi Indurthi and Bakhtiyar Syed Harvey Mudd College at SemEval-2019 Task 4: The Carl Kolchak Hyperpartisan News Detector Celena Chen, Celine Park, Jason Dwyer and Julie Medero Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector Mehdi Drissi, Pedro Sandoval Segura, Vivaswat Ojha and Julie Medero Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector Evan Amason, Jake Palanker, Mary Clare Shen and Julie Medero NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector Duc-Vu Nguyen, Thin Dang and Ngan Nguyen Orwellian-times at SemEval-2019 Task 4: A Stylistic and Content-based Classifier J\u00fcrgen Knauth Rouletabille at SemEval-2019 Task 4: Neural Network Baseline for Identification of Hyperpartisan Publishers Jose G. Moreno, Yoann Pitarch, Karen Pinel-Sauvagnat and Gilles Hubert Spider-Jerusalem at SemEval-2019 Task 4: Hyperpartisan News Detection Amal Alabdulkarim and Tariq Alhindi",
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 "BIBREF139": {
 "ref_id": "b139",
 "title": "Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News Youngjun Joo and Inchon Hwang xl Friday",
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 "raw_text": "Steve Martin at SemEval-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News Youngjun Joo and Inchon Hwang xl Friday, June 7, 2019 (continued)",
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 "ref_entries": {
 "FIGREF0": {
 "text": "SemEval-2019 was co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2019) in Minneapolis, Minnesota, USA. It included the following 11 shared tasks organized in five tracks: \u2022 Frame Semantics and Semantic Parsing -Task 1: Cross-lingual Semantic Parsing with UCCA -Task 2: Unsupervised Lexical Semantic Frame Induction \u2022 Opinion, Emotion and Abusive Language Detection -Task 3: EmoContext: Contextual Emotion Detection in Text -Task 4: Hyperpartisan News Detection -Task 5: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter -Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media \u2022 Fact vs. Fiction -Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours -Task 8: Fact Checking in Community Question Answering Forums \u2022 Information Extraction and Question Answering -Task 9: Suggestion Mining from Online Reviews and Forums -Task 10: Math Question Answering \u2022 NLP for Scientific Applications -Task 12: Toponym Resolution in Scientific Papers iii",
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