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| "paper_id": "2021", |
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| "title": "Organizing Committee", |
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| "text": "Welcome to the Workshop on Cognitive Modeling and Computational Linguistics (CMCL)!!", |
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| "section": "Introduction", |
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| "text": "We reached the 11th edition of CMCL, the workshop of reference for the research at the intersection between Computational Linguistics and Cognitive Science. This is the 2nd edition in a row that will be held entirely online because of the COVID-19 pandemic. Although we won't have the possibility of meeting in person in charming Mexico City, the program of CMCL 2021 is one of the richest and most interesting in the recent history of the workshop. We received 26 regular paper submissions and 17 were accepted for publication, for a total acceptance rate of 65.3%. We also received 4 non-archival submissions (extended abstracts or cross-submissions), 2 of which were accepted for presentation. This year's accepted papers spanned a highly diverse range of questions centering on language, cognition, and computation. Several papers unified computational methods with neurobehavioral data, including EEG, MEG, and fMRI. Many of the papers leveraged state-of-the-art, transformer-based language models to distinguish between two competing theories of sentence processing. Still others probed the differences between language comprehension and language production, and whether it is feasible to treat them similarly for the purposes of explaining language use. Outside of sentence processing, accepted papers also probed the relationship between language and emotion; the graph structure of phonology; and lexical comprehension. Accepted papers spanned several grammatical formalisms, including Combinatory Categorial Grammar, Construction Grammar, and dependency grammars, in addition to statistical approaches. These diverse perspectives on cognition modeling and computational linguistics promote our scientific community's continued growth.", |
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| "text": "Additionally, as a novelty of this year's edition, we have organized a shared task on eye-tracking data prediction for English, and we accepted 10 system description papers. The ability to accurately model gaze features is vital to advance our understanding of language processing. Therefore, we posed the challenge of predicting token-level eye-tracking metrics recorded during natural reading. The participating teams submitted predictions generated mainly with two approaches: (1) Tree-based boosting algorithms with extensive feature engineering and (2) neural networks trained for regression such as fine-tuning transformer-based language models. The features for training the systems included surface features, lexical and syntactic features, token probability features, and text complexity metrics, as well as representations from state-of-the-art language models, such as BERT, RoBERTa, and XLNet. The winning team presented a linguistic feature-based approach.", |
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| "section": "Introduction", |
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| "text": "Also for this year, the contribution of our PC members in thoroughly reviewing and selecting the best papers has been invaluable. Here we wish to deeply thank all of them for their time and effort.", |
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| "section": "Introduction", |
| "sec_num": null |
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| "text": "We also thank Afra Alishahi and Zoya Bylinskii, our keynote speakers, for having accepted our invitation.", |
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| "section": "Introduction", |
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| "text": "Finally, thanks again to our sponsors: the Japanese Society for the Promotion of Sciences and the Laboratoire Parole et Langage. Through their generous support, we have been able to offer fee waivers to PhD students who were first authors of accepted papers, and to offset the participation costs of the invited speakers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 LangResearchLab_NC at CMCL2021 Shared Task: Predicting Gaze Behaviour Using Linguistic Features and Tree Regressors Raksha Agarwal and Niladri Chatterjee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction Bai Li and Frank Rudzicz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach Yves Bestgen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Team ", |
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| "section": "Introduction", |
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| "text": "June 10, 2021, Mexico City (GMT-5) (continued) 17:30-17:45 Closing Remarks xiii", |
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| "back_matter": [ |
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| "text": "June 10, 2021, Mexico City (GMT-5) 9:00-9:15 ", |
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| "section": "Conference Program", |
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| "ref_id": "b0", |
| "title": "KonTra at CMCL 2021 Shared Task: Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information Qi Yu", |
| "authors": [ |
| { |
| "first": "Aikaterini-", |
| "middle": [], |
| "last": "", |
| "suffix": "" |
| }, |
| { |
| "first": "Lida", |
| "middle": [], |
| "last": "Kalouli", |
| "suffix": "" |
| }, |
| { |
| "first": "Diego", |
| "middle": [], |
| "last": "Frassinelli", |
| "suffix": "" |
| }, |
| { |
| "first": ".", |
| "middle": [ |
| "." |
| ], |
| "last": "", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "KonTra at CMCL 2021 Shared Task: Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information Qi Yu, Aikaterini-Lida Kalouli and Diego Frassinelli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformerbased Language Models for Predicting Eye Tracking Patterns Peter Vickers", |
| "authors": [ |
| { |
| "first": "Cognlp-Sheffield ; Rosa", |
| "middle": [], |
| "last": "Wainwright", |
| "suffix": "" |
| }, |
| { |
| "first": "Tayyar", |
| "middle": [], |
| "last": "Harish", |
| "suffix": "" |
| }, |
| { |
| "first": "Aline", |
| "middle": [], |
| "last": "Madabushi", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [ |
| ". . . ." |
| ], |
| "last": "Villavicencio", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer- based Language Models for Predicting Eye Tracking Patterns Peter Vickers, Rosa Wainwright, Harish Tayyar Madabushi and Aline Villavicencio . . . . . . . . . . 125", |
| "links": null |
| }, |
| "BIBREF2": { |
| "ref_id": "b2", |
| "title": "Team ReadMe at CMCL 2021 Shared Task: Predicting Human Reading Patterns by Traditional Oculomotor Control Models and Machine Learning Alisan Balkoca, Abdullah Algan, Cengiz Acarturk and \u00c7agr\u0131 \u00c7\u00f6ltekin", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Team ReadMe at CMCL 2021 Shared Task: Predicting Human Reading Patterns by Traditional Oculo- motor Control Models and Machine Learning Alisan Balkoca, Abdullah Algan, Cengiz Acarturk and \u00c7agr\u0131 \u00c7\u00f6ltekin . . . . . . . . . . . . . . . . . . . . . . 134", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "Enhancing Cognitive Models of Emotions with Representation Learning Yuting", |
| "authors": [ |
| { |
| "first": "Jinho", |
| "middle": [ |
| "D" |
| ], |
| "last": "Guo", |
| "suffix": "" |
| }, |
| { |
| "first": ".", |
| "middle": [ |
| "." |
| ], |
| "last": "Choi", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Enhancing Cognitive Models of Emotions with Representation Learning Yuting Guo and Jinho D. Choi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141", |
| "links": null |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction Jonathan Dunn and Andrea Nini", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction Jonathan Dunn and Andrea Nini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149", |
| "links": null |
| }, |
| "BIBREF5": { |
| "ref_id": "b5", |
| "title": "Clause Final Verb Prediction in Hindi: Evidence for Noisy Channel Model of Communication Kartik Sharma", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Clause Final Verb Prediction in Hindi: Evidence for Noisy Channel Model of Communication Kartik Sharma, Niyati Bafna and Samar Husain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .160", |
| "links": null |
| }, |
| "BIBREF6": { |
| "ref_id": "b6", |
| "title": "Dependency Locality and Neural Surprisal as Predictors of Processing Difficulty: Evidence from Reading Times Neil Rathi", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Dependency Locality and Neural Surprisal as Predictors of Processing Difficulty: Evidence from Read- ing Times Neil Rathi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171", |
| "links": null |
| }, |
| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "Modeling Sentence Comprehension Deficits in Aphasia: A Computational Evaluation of the Directaccess Model of Retrieval Paula Liss\u00f3n, Dorothea Pregla, Dario Paape, Frank Burchert", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Modeling Sentence Comprehension Deficits in Aphasia: A Computational Evaluation of the Direct- access Model of Retrieval Paula Liss\u00f3n, Dorothea Pregla, Dario Paape, Frank Burchert, Nicole Stadie and Shravan Vasishth 177", |
| "links": null |
| }, |
| "BIBREF8": { |
| "ref_id": "b8", |
| "title": "Context Benedetta Iavarone", |
| "authors": [ |
| { |
| "first": "Dominique", |
| "middle": [], |
| "last": "Brunato", |
| "suffix": "" |
| }, |
| { |
| "first": "Felice", |
| "middle": [], |
| "last": "Dell'orletta", |
| "suffix": "" |
| }, |
| { |
| "first": ".", |
| "middle": [ |
| "." |
| ], |
| "last": "", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Sentence Complexity in Context Benedetta Iavarone, Dominique Brunato and Felice Dell'Orletta . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186", |
| "links": null |
| }, |
| "BIBREF9": { |
| "ref_id": "b9", |
| "title": "Evaluating the Acquisition of Semantic Knowledge from Cross-situational Learning in Artificial Neural Networks Mitja Nikolaus and", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Evaluating the Acquisition of Semantic Knowledge from Cross-situational Learning in Artificial Neural Networks Mitja Nikolaus and Abdellah Fourtassi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200", |
| "links": null |
| }, |
| "BIBREF10": { |
| "ref_id": "b10", |
| "title": "Representation and Pre-Activation of Lexical-Semantic Knowledge in Neural Language Models", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Representation and Pre-Activation of Lexical-Semantic Knowledge in Neural Language Models Steven Derby, Barry Devereux and Paul miller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211", |
| "links": null |
| }, |
| "BIBREF11": { |
| "ref_id": "b11", |
| "title": "Relation Classification with Cognitive Attention Supervision Erik", |
| "authors": [ |
| { |
| "first": "Noriko", |
| "middle": [], |
| "last": "Mcguire", |
| "suffix": "" |
| }, |
| { |
| "first": ".", |
| "middle": [ |
| "." |
| ], |
| "last": "Tomuro", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Relation Classification with Cognitive Attention Supervision Erik McGuire and Noriko Tomuro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222", |
| "links": null |
| }, |
| "BIBREF12": { |
| "ref_id": "b12", |
| "title": "Graph-theoretic Properties of the Class of Phonological Neighbourhood Networks Rory", |
| "authors": [ |
| { |
| "first": ".", |
| "middle": [ |
| "." |
| ], |
| "last": "Turnbull", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Graph-theoretic Properties of the Class of Phonological Neighbourhood Networks Rory Turnbull . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233", |
| "links": null |
| }, |
| "BIBREF13": { |
| "ref_id": "b13", |
| "title": "Contributions of Propositional Content and Syntactic Category Information in Sentence Processing Byung-Doh Oh and", |
| "authors": [], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Contributions of Propositional Content and Syntactic Category Information in Sentence Processing Byung-Doh Oh and William Schuler. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .241", |
| "links": null |
| }, |
| "BIBREF14": { |
| "ref_id": "b14", |
| "title": "Mexico City (GMT-5) (continued) PIHKers at CMCL 2021 Shared Task: Cosine Similarity and Surprisal to Predict Human Reading Patterns. Lavinia Salicchi and Alessandro Lenci TALEP at CMCL 2021 Shared Task: Non Linear Combination of Low and High-Level Features for Predicting Eye-Tracking Data Franck Dary, Alexis Nasr and Abdellah Fourtassi MTL782_IITD at CMCL 2021 Shared Task: Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features Shivani Choudhary, Kushagri Tandon, Raksha Agarwal and Niladri Chatterjee KonTra at CMCL 2021 Shared Task: Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information Qi Yu, Aikaterini-Lida Kalouli and Diego Frassinelli CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns Peter Vickers, Rosa Wainwright, Harish Tayyar Madabushi and Aline Villavicencio Team ReadMe at CMCL 2021 Shared Task: Predicting Human Reading Patterns by Traditional Oculomotor Control Models and Machine Learning Alisan Balkoca, Abdullah Algan, Cengiz Acarturk and \u00c7agr\u0131 \u00c7\u00f6ltekin Enhancing Cognitive Models of Emotions with Representation Learning Yuting Guo and Jinho D. Choi Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction Jonathan Dunn and Andrea Nini Clause Final Verb Prediction in Hindi: Evidence for Noisy Channel Model of Communication Kartik Sharma, Niyati Bafna and Samar Husain Dependency Locality and Neural Surprisal as Predictors of Processing Difficulty: Evidence from Reading Times Neil Rathi Modeling Sentence Comprehension Deficits", |
| "authors": [ |
| { |
| "first": "Emmanuele", |
| "middle": [], |
| "last": "Chersoni", |
| "suffix": "" |
| }, |
| { |
| "first": "Cassandra", |
| "middle": [ |
| "L" |
| ], |
| "last": "Jacobs", |
| "suffix": "" |
| }, |
| { |
| "first": "Yohei", |
| "middle": [], |
| "last": "Oseki", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "Oral Presentations 2 A Multinomial Processing Tree Model of RC Attachment Pavel Logacev and Noyan Dokudan That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models Gabriele Sarti, Dominique Brunato and Felice Dell'Orletta Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention Soo Hyun Ryu and Richard Lewis", |
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| "issue": "", |
| "pages": "0--16", |
| "other_ids": {}, |
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| "raw_text": "June 10, 2021, Mexico City (GMT-5) (continued) 12:00-13:00 Lunch break 13:00-14:30 Oral Presentations 2 A Multinomial Processing Tree Model of RC Attachment Pavel Logacev and Noyan Dokudan That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models Gabriele Sarti, Dominique Brunato and Felice Dell'Orletta Accounting for Agreement Phenomena in Sentence Comprehension with Trans- former Language Models: Effects of Similarity-based Interference on Surprisal and Attention Soo Hyun Ryu and Richard Lewis 14:30-14:45 Break 14:45-15:00 Shared Task Presentation CMCL 2021 Shared Task on Eye-Tracking Prediction Nora Hollenstein, Emmanuele Chersoni, Cassandra L. Jacobs, Yohei Oseki, Laurent Pr\u00e9vot and Enrico Santus 15:00-16:30 Poster Session LangResearchLab_NC at CMCL2021 Shared Task: Predicting Gaze Behaviour Us- ing Linguistic Features and Tree Regressors Raksha Agarwal and Niladri Chatterjee TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction Bai Li and Frank Rudzicz LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach Yves Bestgen Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye- Tracking Data Prediction Byung-Doh Oh x June 10, 2021, Mexico City (GMT-5) (continued) PIHKers at CMCL 2021 Shared Task: Cosine Similarity and Surprisal to Predict Human Reading Patterns. Lavinia Salicchi and Alessandro Lenci TALEP at CMCL 2021 Shared Task: Non Linear Combination of Low and High- Level Features for Predicting Eye-Tracking Data Franck Dary, Alexis Nasr and Abdellah Fourtassi MTL782_IITD at CMCL 2021 Shared Task: Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features Shivani Choudhary, Kushagri Tandon, Raksha Agarwal and Niladri Chatterjee KonTra at CMCL 2021 Shared Task: Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information Qi Yu, Aikaterini-Lida Kalouli and Diego Frassinelli CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Fea- tures with Transformer-based Language Models for Predicting Eye Tracking Pat- terns Peter Vickers, Rosa Wainwright, Harish Tayyar Madabushi and Aline Villavicencio Team ReadMe at CMCL 2021 Shared Task: Predicting Human Reading Patterns by Traditional Oculomotor Control Models and Machine Learning Alisan Balkoca, Abdullah Algan, Cengiz Acarturk and \u00c7agr\u0131 \u00c7\u00f6ltekin Enhancing Cognitive Models of Emotions with Representation Learning Yuting Guo and Jinho D. Choi Production vs Perception: The Role of Individuality in Usage-Based Grammar In- duction Jonathan Dunn and Andrea Nini Clause Final Verb Prediction in Hindi: Evidence for Noisy Channel Model of Com- munication Kartik Sharma, Niyati Bafna and Samar Husain Dependency Locality and Neural Surprisal as Predictors of Processing Difficulty: Evidence from Reading Times Neil Rathi Modeling Sentence Comprehension Deficits in Aphasia: A Computational Evalua- tion of the Direct-access Model of Retrieval Paula Liss\u00f3n, Dorothea Pregla, Dario Paape, Frank Burchert, Nicole Stadie and Shravan Vasishth Sentence Complexity in Context Benedetta Iavarone, Dominique Brunato and Felice Dell'Orletta", |
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| "text": "The CMCL 2021 Organizing Committee Rachel A Ryskin, University of California Merced Lavinia Salicchi, The Hong Kong Polytechnic University Marco Senaldi, McGill University Friederike Seyfried, The Hong Kong Polytechnic University William Schuler, Ohio State University Cory Shain, Ohio State University Lonneke Van Der Plas, University of Malta Yao Yao, The Hong Kong Polytechnic University" |
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| "text": "Non-Complementarity of Information in Word-Embedding and Brain Representations in Distinguishing between Concrete and Abstract WordsKalyan Ramakrishnan and Fatma Deniz .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 HumanSentence Processing: Recurrence or Attention? Danny Merkx and Stefan L. Frank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Modeling Incremental Language Comprehension in the Brain with Combinatory Categorial Grammar Milo\u0161 Stanojevi\u0107, Shohini Bhattasali, Donald Dunagan, Luca Campanelli, Mark Steedman, Jonathan Brennan and John Hale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 A Multinomial Processing Tree Model of RC Attachment Pavel Logacev and Noyan Dokudan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models Gabriele Sarti, Dominique Brunato and Felice Dell'Orletta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention Soo Hyun Ryu and Richard Lewis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61" |
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| "text": "Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction Byung-Doh Oh .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 PIHKers at CMCL 2021 Shared Task: Cosine Similarity and Surprisal to Predict Human Reading Patterns. Lavinia Salicchi and Alessandro Lenci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 TALEP at CMCL 2021 Shared Task: Non Linear Combination of Low and High-Level Features for Predicting Eye-Tracking Data Franck Dary, Alexis Nasr and Abdellah Fourtassi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 MTL782_IITD at CMCL 2021 Shared Task: Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features Shivani Choudhary, Kushagri Tandon, Raksha Agarwal and Niladri Chatterjee . . . . . . . . . . . . . . . 114" |
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