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| "text": "Welcome to SemEval-2020! The Semantic Evaluation (SemEval) series of workshops focuses on the evaluation and comparison of systems that can analyze diverse semantic phenomena in text, with the aims 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-2020 is the fourteenth 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 expanding in the number of languages offered, the number of tasks, and also the number of teams participating. 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 became a yearly event. It currently takes place every year, on a two-year cycle. The tasks for SemEval-2020 were proposed in 2019, and next year's tasks have already been selected and are underway.", |
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| "start": 151, |
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| "text": "SensEval-2 (2001), and", |
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| "start": 174, |
| "end": 191, |
| "text": "SensEval-3 (2004)", |
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| "section": "Introduction", |
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| "text": "SemEval-2020 was co-located (virtually) with COLING 2020 -the 28th International Conference on Computational Linguistics -on December 12 and 13, 2020. This year's SemEval included the following 12 tasks, organized into four tracks: 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 the tasks. A total of 12 task description papers and 288 system description papers are included in this volume.", |
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| "section": "Introduction", |
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| "text": "iii SemEval-2020 introduced two new awards, one for organizers of a task and one for a team participating in a task. The Best Task award recognizes a task that stands out for making an important intellectual contribution to empirical computational semantics, as demonstrated by a creative, interesting, and scientifically rigorous dataset and evaluation design, and a well-written task overview paper. The Best Paper award recognizes a system description paper (written by a team participating in one of the tasks) that advances our understanding of a problem and available solutions with respect to a task. It needs not be the highest-scoring system in the task, but it must have a strong analysis component in the evaluation, as well as a clear and reproducible description of the problem, algorithms, and methodology. 2020 has been an especially challenging year across the globe, and SemEval-2020 saw its fair share of delays and unexpected changes. We are immensely grateful to the task organizers for their perseverance through many ups, downs, and uncertainties, as well as to the large number of participants whose enthusiastic participation has made SemEval once again a successful event! Thanks also to the task organizers who served as area chairs for their tasks, and to both task organizers and participants who reviewed paper submissions. These proceedings have greatly benefited from their detailed and thoughtful feedback. Thousands of thanks to our assistant organizers Shabnam Behzad and Michael Kranzlein for their extensive, detailed, and dedicated work on the production of these proceedings! We also thank the members of the program committee who reviewed the submitted task proposals and helped us to select this exciting set of tasks, and we thank the COLING 2020 conference organizers for their support. Finally, we most gratefully acknowledge the support of our sponsor: the ACL Special Interest Group on the Lexicon (SIGLEX).", |
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| "raw_text": "SalamNET at SemEval-2020 Task 12: Deep Learning Approach for Arabic Offensive Language Detec- tion Fatemah Husain, Jooyeon Lee, Sam Henry and Ozlem Uzuner . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2133", |
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| "title": "SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation Qian Zhao, Siyu Tao, Jie Zhou, Linlin Wang, Xin Lin and Liang He Masked Reasoner at SemEval-2020 Task 4: Fine-Tuning RoBERTa for Commonsense Reasoning Daming Lu QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation System Based on Ensemble of Language Model Liu Pai SWAGex at SemEval-2020 Task 4: Commonsense Explanation as Next Event Prediction Wiem Ben Rim and Naoaki Okazaki UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation Thanet Markchom, Bhuvana Dhruva, Chandresh Pravin and Huizhi Liang BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models Martin Fajcik", |
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| "venue": "SemEval-2020 Task 4: Multilingual Commonsense Josef Jon, Martin Fajcik, Martin Docekal and Pavel Smrz CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning Hongru Wang, Xiangru Tang", |
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| "raw_text": "Smatgrisene at SemEval-2020 Task 12: Offense Detection by AI -with a Pinch of Real I 12/12/2020 (continued) 16:00-17:00 Invited talk: Afra Alishahi 17:00-17:30 Common Sense Knowledge and Reasoning, Knowledge Extraction: oral ses- sion SemEval-2020 Task 4: Commonsense Validation and Explanation Cunxiang Wang, Shuailong Liang, Yili Jin, Yilong Wang, Xiaodan Zhu and Yue Zhang SemEval-2020 Task 5: Counterfactual Recognition Xiaoyu Yang, Stephen Obadinma, Huasha Zhao, Qiong Zhang, Stan Matwin and Xiaodan Zhu SemEval-2020 Task 6: Definition Extraction from Free Text with the DEFT Corpus Sasha Spala, Nicholas Miller, Franck Dernoncourt and Carl Dockhorn IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Re- construction Strategy for ComVE Luxi Xing, Yuqiang Xie, Yue Hu and Wei Peng HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection Xiao Ding, Dingkui Hao, Yuewei Zhang, Kuo Liao, Zhongyang Li, Bing Qin and Ting Liu Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification Shelan Jeawak, Luis Espinosa-Anke and Steven Schockaert xxxiii 12/12/2020 (continued) 17:30-19:00 Common Sense Knowledge and Reasoning, Knowledge Extraction: poster ses- sion ANA at SemEval-2020 Task 4: MUlti-task learNIng for cOmmonsense reasoNing (UNION) Anandh Konar, Chenyang Huang, Amine Trabelsi and Osmar Zaiane BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense Josef Jon, Martin Fajcik, Martin Docekal and Pavel Smrz CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Pre- diction with Multi-task Learning Hongru Wang, Xiangru Tang, Sunny Lai, Kwong Sak Leung, Jia Zhu, Gabriel Pui Cheong Fung and Kam-Fai Wong ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowl- edge Resources for Commonsense Validation and Explanation Qian Zhao, Siyu Tao, Jie Zhou, Linlin Wang, Xin Lin and Liang He Masked Reasoner at SemEval-2020 Task 4: Fine-Tuning RoBERTa for Common- sense Reasoning Daming Lu QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation Sys- tem Based on Ensemble of Language Model Liu Pai SWAGex at SemEval-2020 Task 4: Commonsense Explanation as Next Event Pre- diction Wiem Ben Rim and Naoaki Okazaki UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Com- monsense Validation and Explanation Thanet Markchom, Bhuvana Dhruva, Chandresh Pravin and Huizhi Liang BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual State- ments with Deep Pre-trained Language Representation Models Martin Fajcik, Josef Jon, Martin Docekal and Pavel Smrz CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs MinGyou Sung, Parsa Bagherzadeh and Sabine Bergler CNRL at SemEval-2020 Task 5: Modelling Causal Reasoning in Language with Multi-Head Self-Attention Weights Based Counterfactual Detection Rajaswa Patil and Veeky Baths xxxiv 12/12/2020 (continued)", |
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| "BIBREF178": { |
| "ref_id": "b178", |
| "title": "Detecting Counterfactuals Anirudh Anil Ojha, Rohin Garg, Shashank Gupta and Ashutosh Modi Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis Hanna Abi-Akl", |
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| "raw_text": "IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals Anirudh Anil Ojha, Rohin Garg, Shashank Gupta and Ashutosh Modi Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfac- tual Statement Analysis Hanna Abi-Akl, Dominique Mariko and Estelle Labidurie", |
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| "text": "Lexical semantics -Task 1: Unsupervised Lexical Semantic Change Detection -Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment -Task 3: Graded Word Similarity in Context \u2022 Common Sense Knowledge and Reasoning, Knowledge Extraction -Task 4: Commonsense Validation and Explanation -Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals -Task 6: DeftEval: Extracting Definitions from Free Text in Textbooks \u2022 Humour, Emphasis, and Sentiment -Task 7: Assessing Humor in Edited News Headlines -Task 8: Memotion Analysis -Task 9: Sentiment Analysis for Code-Mixed Social Media Text -Task 10: Emphasis Selection for Written Text in Visual Media \u2022 Societal Applications of NLP -Task 11: Detection of Propaganda Techniques in News Articles -Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media" |
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