{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T05:23:02Z","timestamp":1755926582401,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"DOI":"10.1145\/3442381.3449806","type":"proceedings-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T19:03:16Z","timestamp":1622746996000},"page":"3188-3198","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Situation and Behavior Understanding by Trope Detection on Films"],"prefix":"10.1145","author":[{"given":"Chen-Hsi","family":"Chang","sequence":"first","affiliation":[{"name":"National Taiwan University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hung-Ting","family":"Su","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jui-Heng","family":"Hsu","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Siang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Toronto, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Cheng","family":"Chang","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya-Liang","family":"Chang","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Feng","family":"Cheng","sequence":"additional","affiliation":[{"name":"National Taiwan University and Microsoft, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke-Jyun","family":"Wang","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Winston H.","family":"Hsu","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"volume-title":"ICCV Workshops.","author":"Jasani Girdhar","key":"e_1_3_2_1_1_1","unstructured":"R.\u00a0 Girdhar B.\u00a0 Jasani and D. Ramanan . 2019. Are we asking the right questions in MovieQA? . In ICCV Workshops. R.\u00a0Girdhar B.\u00a0Jasani and D. Ramanan. 2019. Are we asking the right questions in MovieQA?. In ICCV Workshops."},{"key":"e_1_3_2_1_2_1","volume-title":"Systematic Generalization: What Is Required and Can It Be Learned?. In ICLR.","author":"Bahdanau Dzmitry","year":"2019","unstructured":"Dzmitry Bahdanau , Shikhar Murty , Michael Noukhovitch , Thien\u00a0Huu Nguyen , Harm de Vries , and Aaron Courville . 2019 . Systematic Generalization: What Is Required and Can It Be Learned?. In ICLR. Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien\u00a0Huu Nguyen, Harm de Vries, and Aaron Courville. 2019. Systematic Generalization: What Is Required and Can It Be Learned?. In ICLR."},{"key":"e_1_3_2_1_3_1","volume-title":"From System 1 Deep Learning to System 2 Deep Learning. NeuripS","author":"Bengio Yoshua","year":"2019","unstructured":"Yoshua Bengio . 2019. From System 1 Deep Learning to System 2 Deep Learning. NeuripS ( 2019 ). Yoshua Bengio. 2019. From System 1 Deep Learning to System 2 Deep Learning. NeuripS (2019)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Eunsol Choi He He Mohit Iyyer Mark Yatskar Wen-tau Yih Yejin Choi Percy Liang and Luke Zettlemoyer. 2018. QuAC: Question Answering in Context. In EMNLP.  Eunsol Choi He He Mohit Iyyer Mark Yatskar Wen-tau Yih Yejin Choi Percy Liang and Luke Zettlemoyer. 2018. QuAC: Question Answering in Context. In EMNLP.","DOI":"10.18653\/v1\/D18-1241"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Kevin Clark and Christopher\u00a0D. Manning. 2016. Deep Reinforcement Learning for Mention-Ranking Coreference Models. In EMNLP.  Kevin Clark and Christopher\u00a0D. Manning. 2016. Deep Reinforcement Learning for Mention-Ranking Coreference Models. In EMNLP.","DOI":"10.18653\/v1\/D16-1245"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Kevin Clark and Christopher\u00a0D. Manning. 2016. Improving Coreference Resolution by Learning Entity-Level Distributed Representations. In ACL.  Kevin Clark and Christopher\u00a0D. Manning. 2016. Improving Coreference Resolution by Learning Entity-Level Distributed Representations. In ACL.","DOI":"10.18653\/v1\/P16-1061"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"J. Deng W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR.  J. Deng W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_8_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT.","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Jesse Dunietz Greg Burnham Akash Bharadwaj Owen Rambow Jennifer Chu-Carroll and Dave Ferrucci. 2020. To Test Machine Comprehension Start by Defining Comprehension. In ACL.  Jesse Dunietz Greg Burnham Akash Bharadwaj Owen Rambow Jennifer Chu-Carroll and Dave Ferrucci. 2020. To Test Machine Comprehension Start by Defining Comprehension. In ACL.","DOI":"10.18653\/v1\/2020.acl-main.701"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Allyson Ettinger. 2020. What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models. TACL 8(2020).  Allyson Ettinger. 2020. What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models. TACL 8(2020).","DOI":"10.1162\/tacl_a_00298"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Emily Goodwin Koustuv Sinha and Timothy\u00a0J. O\u2019Donnell. 2020. Probing Linguistic Systematicity. In ACL.  Emily Goodwin Koustuv Sinha and Timothy\u00a0J. O\u2019Donnell. 2020. Probing Linguistic Systematicity. In ACL.","DOI":"10.18653\/v1\/2020.acl-main.177"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Arthur\u00a0C Graesser Murray Singer and Tom Trabasso. 1994. Constructing inferences during narrative text comprehension. Psychological Review(1994).  Arthur\u00a0C Graesser Murray Singer and Tom Trabasso. 1994. Constructing inferences during narrative text comprehension. Psychological Review(1994).","DOI":"10.1037\/0033-295X.101.3.371"},{"key":"e_1_3_2_1_13_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ). Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997)."},{"key":"e_1_3_2_1_14_1","unstructured":"Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings convolutional neural networks and incremental parsing. (2017).  Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings convolutional neural networks and incremental parsing. (2017)."},{"key":"e_1_3_2_1_15_1","volume-title":"MPST: A Corpus of Movie Plot Synopses with Tags. In LREC.","author":"Kar Sudipta","year":"2018","unstructured":"Sudipta Kar , Suraj Maharjan , A.\u00a0 Pastor L\u00f3pez-Monroy , and Thamar Solorio . 2018 . MPST: A Corpus of Movie Plot Synopses with Tags. In LREC. Sudipta Kar, Suraj Maharjan, A.\u00a0Pastor L\u00f3pez-Monroy, and Thamar Solorio. 2018. MPST: A Corpus of Movie Plot Synopses with Tags. In LREC."},{"key":"e_1_3_2_1_16_1","volume-title":"Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network. In COLING.","author":"Kar Sudipta","year":"2018","unstructured":"Sudipta Kar , Suraj Maharjan , and Thamar Solorio . 2018 . Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network. In COLING. Sudipta Kar, Suraj Maharjan, and Thamar Solorio. 2018. Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network. In COLING."},{"key":"e_1_3_2_1_17_1","unstructured":"Kyung-Min Kim Min-Oh Heo Seong-Ho Choi and Byoung-Tak Zhang. 2017. DeepStory: Video Story QA by Deep Embedded Memory Networks. In IJCAI.  Kyung-Min Kim Min-Oh Heo Seong-Ho Choi and Byoung-Tak Zhang. 2017. DeepStory: Video Story QA by Deep Embedded Memory Networks. In IJCAI."},{"key":"e_1_3_2_1_18_1","volume-title":"RACE: Large-scale ReAding Comprehension Dataset From Examinations. In EMNLP.","author":"Lai Guokun","year":"2017","unstructured":"Guokun Lai , Qizhe Xie , Hanxiao Liu , Yiming Yang , and Eduard Hovy . 2017 . RACE: Large-scale ReAding Comprehension Dataset From Examinations. In EMNLP. Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. RACE: Large-scale ReAding Comprehension Dataset From Examinations. In EMNLP."},{"key":"e_1_3_2_1_19_1","volume-title":"Lake and Marco Baroni","author":"M.","year":"2018","unstructured":"Brenden\u00a0 M. Lake and Marco Baroni . 2018 . Generalization without Systematicity : On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. In ICML. Brenden\u00a0M. Lake and Marco Baroni. 2018. Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. In ICML."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Jens Lehmann Robert Isele Max Jakob Anja Jentzsch Dimitris Kontokostas Pablo\u00a0N. Mendes Sebastian Hellmann Mohamed Morsey Patrick van Kleef S\u00f6ren Auer and Christian Bizer. 2015. DBpedia - A Large-scale Multilingual Knowledge Base Extracted from Wikipedia. Semantic web 6(2015).  Jens Lehmann Robert Isele Max Jakob Anja Jentzsch Dimitris Kontokostas Pablo\u00a0N. Mendes Sebastian Hellmann Mohamed Morsey Patrick van Kleef S\u00f6ren Auer and Christian Bizer. 2015. DBpedia - A Large-scale Multilingual Knowledge Base Extracted from Wikipedia. Semantic web 6(2015).","DOI":"10.3233\/SW-140134"},{"key":"e_1_3_2_1_21_1","volume-title":"TVQA: Localized, Compositional Video Question Answering. In EMNLP.","author":"Lei Jie","year":"2018","unstructured":"Jie Lei , Licheng Yu , Mohit Bansal , and Tamara Berg . 2018 . TVQA: Localized, Compositional Video Question Answering. In EMNLP. Jie Lei, Licheng Yu, Mohit Bansal, and Tamara Berg. 2018. TVQA: Localized, Compositional Video Question Answering. In EMNLP."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Tal Linzen. 2020. How Can We Accelerate Progress Towards Human-like Linguistic Generalization?. In ACL.  Tal Linzen. 2020. How Can We Accelerate Progress Towards Human-like Linguistic Generalization?. In ACL.","DOI":"10.18653\/v1\/2020.acl-main.465"},{"key":"e_1_3_2_1_23_1","volume-title":"Violin: A Large-Scale Dataset for Video-and-Language Inference. In CVPR.","author":"Liu Jingzhou","year":"2020","unstructured":"Jingzhou Liu , Wenhu Chen , Yu Cheng , Zhe Gan , Licheng Yu , Yiming Yang , and Jingjing Liu . 2020 . Violin: A Large-Scale Dataset for Video-and-Language Inference. In CVPR. Jingzhou Liu, Wenhu Chen, Yu Cheng, Zhe Gan, Licheng Yu, Yiming Yang, and Jingjing Liu. 2020. Violin: A Large-Scale Dataset for Video-and-Language Inference. In CVPR."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"R\u00a0Thomas McCoy Junghyun Min and Tal Linzen. 2019. Berts of a feather do not generalize together: Large variability in generalization across models with similar test set performance. arXiv preprint arXiv:1911.02969(2019).  R\u00a0Thomas McCoy Junghyun Min and Tal Linzen. 2019. Berts of a feather do not generalize together: Large variability in generalization across models with similar test set performance. arXiv preprint arXiv:1911.02969(2019).","DOI":"10.18653\/v1\/2020.blackboxnlp-1.21"},{"key":"e_1_3_2_1_25_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg\u00a0S Corrado and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In NeurIPS.  Tomas Mikolov Ilya Sutskever Kai Chen Greg\u00a0S Corrado and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In NeurIPS."},{"key":"e_1_3_2_1_26_1","unstructured":"Rasmus\u00a0Berg Palm Ulrich Paquet and Ole Winther. 2018. Recurrent Relational Networks. In NeurIPS.  Rasmus\u00a0Berg Palm Ulrich Paquet and Ole Winther. 2018. Recurrent Relational Networks. In NeurIPS."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Matthew\u00a0E. Peters Mark Neumann Mohit Iyyer Matt Gardner Christopher Clark Kenton Lee and Luke Zettlemoyer. 2018. Deep contextualized word representations. In NAACL-HLT.  Matthew\u00a0E. Peters Mark Neumann Mohit Iyyer Matt Gardner Christopher Clark Kenton Lee and Luke Zettlemoyer. 2018. Deep contextualized word representations. In NAACL-HLT.","DOI":"10.18653\/v1\/N18-1202"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Matthew\u00a0E. Peters Sebastian Ruder and Noah\u00a0A. Smith. 2019. To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks. In RepL4NLP.  Matthew\u00a0E. Peters Sebastian Ruder and Noah\u00a0A. Smith. 2019. To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks. In RepL4NLP.","DOI":"10.18653\/v1\/W19-4302"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Pranav Rajpurkar Robin Jia and Percy Liang. 2018. Know What You Don\u2019t Know: Unanswerable Questions for SQuAD. In ACL.  Pranav Rajpurkar Robin Jia and Percy Liang. 2018. Know What You Don\u2019t Know: Unanswerable Questions for SQuAD. In ACL.","DOI":"10.18653\/v1\/P18-2124"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Pranav Rajpurkar Jian Zhang Konstantin Lopyrev and Percy Liang. 2016. SQuAD: 100 000+ Questions for Machine Comprehension of Text. In EMNLP.  Pranav Rajpurkar Jian Zhang Konstantin Lopyrev and Percy Liang. 2016. SQuAD: 100 000+ Questions for Machine Comprehension of Text. In EMNLP.","DOI":"10.18653\/v1\/D16-1264"},{"key":"e_1_3_2_1_31_1","unstructured":"Minjoon Seo Aniruddha Kembhavi Ali Farhadi and Hananneh Hajishirzi. 2017. Bidirectional Attention Flow for Machine Comprehension. In ICLR.  Minjoon Seo Aniruddha Kembhavi Ali Farhadi and Hananneh Hajishirzi. 2017. Bidirectional Attention Flow for Machine Comprehension. In ICLR."},{"key":"e_1_3_2_1_32_1","volume-title":"CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text. In EMNLP-IJCNLP.","author":"Sinha Koustuv","year":"2019","unstructured":"Koustuv Sinha , Shagun Sodhani , Jin Dong , Joelle Pineau , and William\u00a0 L. Hamilton . 2019 . CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text. In EMNLP-IJCNLP. Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William\u00a0L. Hamilton. 2019. CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text. In EMNLP-IJCNLP."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"John\u00a0R. Smith Dhiraj Joshi Benoit Huet Winston Hsu and Jozef Cota. 2017. Harnessing A.I. For Augmenting Creativity: Application to Movie Trailer Creation. In ACM MM.  John\u00a0R. Smith Dhiraj Joshi Benoit Huet Winston Hsu and Jozef Cota. 2017. Harnessing A.I. For Augmenting Creativity: Application to Movie Trailer Creation. In ACM MM.","DOI":"10.1145\/3123266.3127906"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Makarand Tapaswi Yukun Zhu Rainer Stiefelhagen Antonio Torralba Raquel Urtasun and Sanja Fidler. 2016. MovieQA: Understanding Stories in Movies through Question-Answering. In CVPR.  Makarand Tapaswi Yukun Zhu Rainer Stiefelhagen Antonio Torralba Raquel Urtasun and Sanja Fidler. 2016. MovieQA: Understanding Stories in Movies through Question-Answering. In CVPR.","DOI":"10.1109\/CVPR.2016.501"},{"key":"e_1_3_2_1_35_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141\u00a0ukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NeurIPS.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141\u00a0ukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NeurIPS."},{"key":"e_1_3_2_1_36_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141\u00a0ukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NeurIPS.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141\u00a0ukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NeurIPS."},{"key":"e_1_3_2_1_37_1","volume-title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. In ICLR.","author":"Wang Alex","year":"2019","unstructured":"Alex Wang , Amanpreet Singh , Julian Michael , Felix Hill , Omer Levy , and Samuel\u00a0 R. Bowman . 2019 . GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. In ICLR. Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel\u00a0R. Bowman. 2019. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. In ICLR."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Johannes Welbl Pontus Stenetorp and Sebastian Riedel. 2018. Constructing Datasets for Multi-hop Reading Comprehension Across Documents. TACL 6(2018).  Johannes Welbl Pontus Stenetorp and Sebastian Riedel. 2018. Constructing Datasets for Multi-hop Reading Comprehension Across Documents. TACL 6(2018).","DOI":"10.1162\/tacl_a_00021"},{"key":"e_1_3_2_1_39_1","unstructured":"Zhilin Yang Zihang Dai Yiming Yang Jaime Carbonell Russ\u00a0R Salakhutdinov and Quoc\u00a0V Le. 2019. XLNet: Generalized Autoregressive Pretraining for Language Understanding. In NeurIPS.  Zhilin Yang Zihang Dai Yiming Yang Jaime Carbonell Russ\u00a0R Salakhutdinov and Quoc\u00a0V Le. 2019. XLNet: Generalized Autoregressive Pretraining for Language Understanding. In NeurIPS."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Zhilin Yang Peng Qi Saizheng Zhang Yoshua Bengio William Cohen Ruslan Salakhutdinov and Christopher\u00a0D. Manning. 2018. HotpotQA: A Dataset for Diverse Explainable Multi-hop Question Answering. In EMNLP.  Zhilin Yang Peng Qi Saizheng Zhang Yoshua Bengio William Cohen Ruslan Salakhutdinov and Christopher\u00a0D. Manning. 2018. HotpotQA: A Dataset for Diverse Explainable Multi-hop Question Answering. In EMNLP.","DOI":"10.18653\/v1\/D18-1259"},{"key":"e_1_3_2_1_41_1","unstructured":"Adams\u00a0Wei Yu David Dohan Quoc Le Thang Luong Rui Zhao and Kai Chen. 2018. Fast and Accurate Reading Comprehension by Combining Self-Attention and Convolution. In ICLR.  Adams\u00a0Wei Yu David Dohan Quoc Le Thang Luong Rui Zhao and Kai Chen. 2018. Fast and Accurate Reading Comprehension by Combining Self-Attention and Convolution. In ICLR."},{"key":"e_1_3_2_1_42_1","volume-title":"SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference. In EMNLP.","author":"Zellers Rowan","year":"2018","unstructured":"Rowan Zellers , Yonatan Bisk , Roy Schwartz , and Yejin Choi . 2018 . SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference. In EMNLP. Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018. SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference. In EMNLP."}],"event":{"name":"WWW '21: The Web Conference 2021","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Ljubljana Slovenia","acronym":"WWW '21"},"container-title":["Proceedings of the Web Conference 2021"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449806","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442381.3449806","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:22Z","timestamp":1750195462000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":42,"alternative-id":["10.1145\/3442381.3449806","10.1145\/3442381"],"URL":"https:\/\/doi.org\/10.1145\/3442381.3449806","relation":{},"subject":[],"published":{"date-parts":[[2021,4,19]]},"assertion":[{"value":"2021-06-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}