Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing

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EMNLP 2019. Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing. November 3, 2019 Hongkong. c©2019 The Association for Computational Linguistics. Order copies of this and other ACL proceedings from:. Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 acl@aclweb.org. ISBN 978-1-950737-79-6. ii. Introduction. Welcome to the first Workshop on Commonsense Inference in Natural Language Processing, COIN.. This workshop takes place for the first time and has a focus on research around modeling commonsense knowledge, developing computational models thereof, and applying commonsense inference methods in NLP tasks. This includes any type of commonsense knowledge representation, and explicitly also work that makes use of knowledge bases and approaches developed to mine or learn commonsense from other sources. Evaluation proposals that explore new ways of evaluating methods of commonsense inference, going beyond established natural language processing tasks are also of interest for the workshop.. The workshop included two shared tasks on English reading comprehension using commonsense knowledge. The first task is a multiple choice reading comprehension task on everyday narrations. The second task is a cloze task on news texts.. Several teams participated in the shared tasks, with 4 teams submitting results for task 1, and one team submitting results for both tasks. All models are based on Transformer architectures. The best performing models reach 90.6% accuracy and 83.7% F1-score on task 1 and task 2, respectively.. In total, we received 22 paper submissions (among them 6 shared task papers), out of which 16 were accepted. All workshop papers are presented as talks, while the shared task papers are presented in a poster session. In addition, the workshop includes two invited talks on the topics of commonsense inference and commonsense in question answering.. The program committee consisted of 21 researchers, who we’d like to thank for providing helpful and constructive reviews on the papers. We’d also like to thank all authors for their submissions and interest in our workshop.. Simon, Sheng, Michael and Peter. iii. Organizers:. Simon Ostermann, Saarland University / Nuance Communications. Sheng Zhang, Johns Hopkins University. Michael Roth, Saarland University / University of Stuttgart. Peter Clark, Allen Institute for AI. Program Committee:. Malihe Alikhani, Rutgers University. Ken Barker, IBM Research. Yonatan Bisk, University of Washington. Nathanael Chambers, United States Naval Academy. Hans Chalupsky, USC Information Sciences Institute. Vera Demberg, Saarland University. Andrew S. Gordon, University of Southern California. Jonathan Gordon, Vassar College. William Jarrold, Nuance Communications. Gerard de Melo, Rutgers University. Elizabeth Merkhofer, MITRE Corporation. Todor Mihaylov, Heidelberg University. Ashutosh Modi, Disney Research. Sreyasi Nag Chowdhury, Max-Planck-Institut for Informatics. Juri Opitz, Heidelberg University. Letitia Elena Parcalabescu, Heidelberg University. Debjit Paul, Heidelberg University. Hannah Rashkin, University of Washington. Simon Razniewski, Max Planck Institute for Informatics. Niket Tandon, Allen Institute for AI. Adam Trischler, Microsoft Research. Invited Speaker:. Yejin Choi, University of Washington / Allen Institute for AI. Michael Witbrock, University of Auckland. v. Table of Contents. Understanding Commonsense Inference Aptitude of Deep Contextual Representations Jeff Da and Jungo Kasai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. A Hybrid Neural Network Model for Commonsense Reasoning Pengcheng He, Xiaodong Liu, Weizhu Chen and Jianfeng Gao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13. Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering Kaixin Ma, Jonathan Francis, Quanyang Lu, Eric Nyberg and Alessandro Oltramari . . . . . . . . . . . 22. When Choosing Plausible Alternatives, Clever Hans can be Clever Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert and Kentaro Inui. 33. Commonsense about Human Senses: Labeled Data Collection Processes Ndapa Nakashole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43. Extracting Common Inference Patterns from Semi-Structured Explanations Sebastian Thiem and Peter Jansen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53. Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report Simon Ostermann, Sheng Zhang, Michael Roth and Peter Clark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66. KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with rela- tional knowledge for machine comprehension with common sense. Yash Jain and Chinmay Singh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75. IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension Prakhar Sharma and Sumegh Roychowdhury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80. Jeff Da at COIN - Shared Task Jeff Da . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85. Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks. Xiepeng Li, Zhexi Zhang, Wei Zhu, Zheng Li, Yuan Ni, Peng Gao, Junchi Yan and Guotong Xie93. BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Every- day Narrations. Chunhua Liu and Dong Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99. Commonsense inference in human-robot communication Aliaksandr Huminski, Yan Bin Ng, Kenneth Kwok and Francis Bond . . . . . . . . . . . . . . . . . . . . . . . 104. Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction Hirokazu Kiyomaru, Kazumasa Omura, Yugo Murawaki, Daisuke Kawahara and Sadao Kurohashi. 113. Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text Ian Porada, Kaheer Suleman and Jackie Chi Kit Cheung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123. How Pre-trained Word Representations Capture Commonsense Physical Comparisons Pranav Goel, Shi Feng and Jordan Boyd-Graber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130. vii. Conference Program. Sunday, November 3, 2019. 9:00–10:30 Morning Session. 9:00–9:10 Introduction. 9:10–10:10 Invited talk: Commonsense Intelligence—Cracking the Longstanding Challenge in AI Yejin Choi. 10:10–10:30 Understanding Commonsense Inference Aptitude of Deep Contextual Representa- tions Jeff Da and Jungo Kasai. 10:30–11:00 Coffee break. 11:00–12:20 Pre-lunch Session. 11:00–11:20 A Hybrid Neural Network Model for Commonsense Reasoning Pengcheng He, Xiaodong Liu, Weizhu Chen and Jianfeng Gao. 11:20–11:40 Towards Generalizable Neuro-Symbolic Systems for Commonsense Question An- swering Kaixin Ma, Jonathan Francis, Quanyang Lu, Eric Nyberg and Alessandro Oltramari. 11:40–12:00 When Choosing Plausible Alternatives, Clever Hans can be Clever Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert and Kentaro Inui. 12:00–12:20 Commonsense about Human Senses: Labeled Data Collection Processes Ndapa Nakashole. 12:20–14:00 Lunch break. ix. Sunday, November 3, 2019 (continued). 14:00–15:20 Post-lunch Session. 14:00–15:00 Invited talk: Learning to Reason: from Question Answering to Problem Solving Michael Witbrock. 15:00–15:20 Extracting Common Inference Patterns from Semi-Structured Explanations Sebastian Thiem and Peter Jansen. 15:20–16:20 Poster session. 15:30–16:00 (including coffee break). 15:20–16:20 Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report Simon Ostermann, Sheng Zhang, Michael Roth and Peter Clark. 15:20–16:20 KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Trans- formers with relational knowledge for machine comprehension with common sense Yash Jain and Chinmay Singh. 15:20–16:20 IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension Prakhar Sharma and Sumegh Roychowdhury. 15:20–16:20 Jeff Da at COIN - Shared Task Jeff Da. 15:20–16:20 Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Lan- guage Models and Common-sense Knowledge in Machine Reading Tasks Xiepeng Li, Zhexi Zhang, Wei Zhu, Zheng Li, Yuan Ni, Peng Gao, Junchi Yan and Guotong Xie. 15:20–16:20 BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations Chunhua Liu and Dong Yu. x. Sunday, November 3, 2019 (continued). 16:20–17:30 Afternoon Session. 16:20–16:40 Commonsense inference in human-robot communication Aliaksandr Huminski, Yan Bin Ng, Kenneth Kwok and Francis Bond. 16:40–16:55 Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction Hirokazu Kiyomaru, Kazumasa Omura, Yugo Murawaki, Daisuke Kawahara and Sadao Kurohashi. 16:55–17:15 Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text Ian Porada, Kaheer Suleman and Jackie Chi Kit Cheung. 17:15–17:30 How Pre-trained Word Representations Capture Commonsense Physical Compar- isons Pranav Goel, Shi Feng and Jordan Boyd-Graber. xi. Program

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