{"type":"ScholarlyArticle","title":"SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition","authors":[{"name":"Wenqiang Lei","slug":"wenqiang-lei","given":"Wenqiang","family":"Lei","affiliation":"National University of Singapore"},{"name":"Xuancong Wang","slug":"xuancong-wang","given":"Xuancong","family":"Wang","affiliation":"Institute for Infocomm Research"},{"name":"Meichun Liu","slug":"meichun-liu","given":"Meichun","family":"Liu","affiliation":"City University of Hong Kong"},{"name":"Ilija Ilievski","slug":"ilija-ilievski","given":"Ilija","family":"Ilievski","affiliation":"National University of Singapore"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"National University of Singapore"},{"name":"Min-Yen Kan","slug":"min-yen-kan","given":"Min-Yen","family":"Kan","affiliation":"National University of Singapore"}],"abstract":"Capturing the semantic interaction of pairs of words across arguments and proper argument representation are both crucial issues in implicit discourse relation recognition. The current state-of-the-art represents  arguments as distributional vectors that are computed via bi-directional Long Short-Term Memory networks (BiLSTMs), known to have significant model complexity.In contrast, we demonstrate that word-weighted averaging can encode argument representation which  can incorporate word pair information efficiently. By saving an order of magnitude in parameters, our proposed model achieves equivalent performance, but trains seven times faster.","doi":"10.24963/ijcai.2017/562","url":"https://ijcai.org/proceedings/2017/562","pdf":"https://ijcai.org/proceedings/2017/0562.pdf","markdown":"https://ijcai.org/proceedings/2017/562.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/562","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Natural Language Processing","track":"Main track","number":562,"pages":{"first":4026,"last":4032},"keywords":["Natural Language Processing: Discourse","Natural Language Processing: Natural Language Processing","Natural Language Processing: Text Classification","Natural Language Processing: Natural Language Semantics"],"videos":[],"references":[{"position":1,"raw":"[Carlson et al., 2002] Lynn Carlson, Mary Ellen Okurowski, and Daniel Marcu. RST discourse treebank. Linguistic Data Consortium, University of Pennsylvania, 2002.","title":"RST discourse treebank","authors":"Lynn Carlson, Mary Ellen Okurowski, Daniel Marcu","year":"2002","venue":"Linguistic Data Consortium","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chandrasekaran et al., 2017] Muthu Kumar Chan- drasekaran, Carrie Demmans Epp, Min-Yen Kan, and Diane Litman. Using discourse signals for robust instructor intervention prediction. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), pages 3415-3421, 2017.","title":"Using discourse signals for robust instructor intervention prediction","authors":"Muthu Kumar Chandrasekaran, Carrie Demmans Epp, Min-Yen Kan, Diane Litman","year":"2017","venue":"Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2016] Jifan Chen, Qi Zhang, Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. Implicit discourse rela- tion detection via a deep architecture with gated relevance network. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016.","title":"Implicit discourse relation detection via a deep architecture with gated relevance network","authors":"Chen","year":"2016","venue":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Duchi et al., 2011] John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(Jul):2121-2159, 2011.","title":"Adaptive subgradient methods for online learning and stochastic optimization","authors":"John Duchi, Elad Hazan, Yoram Singer","year":"2011","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[He et al., 2017] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collab- orative filtering. In 26th International World Wide Web Conference, 2017.","title":"Neural collaborative filtering","authors":"He","year":"2017","venue":"26th International World Wide Web Conference","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hernault et al., 2010] Hugo Hernault, Danushka Bollegala, and Mitsuru Ishizuka. A semi-supervised approach to im- prove classification of infrequent discourse relations using feature vector extension. In Proceedings of the 2010 Con- ference on Empirical Methods in Natural Language Pro- cessing, pages 399-409. Association for Computational Linguistics, 2010.","title":"A semi-supervised approach to improve classification of infrequent discourse relations using feature vector extension","authors":"Hernault","year":"2010","venue":"Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Iyyer et al., 2015] Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. Deep unordered com- position rivals syntactic methods for text classification. In Proceedings of the Association for Computational Lin- guistics, 2015.","title":"Deep unordered composition rivals syntactic methods for text classification","authors":"Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, Hal Daumé, Iii","year":"2015","venue":"Proceedings of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Lan et al., 2013] Man Lan, Yu Xu, Zheng-Yu Niu, et al. Leveraging synthetic discourse data via multi-task learn- ing for implicit discourse relation recognition. In Proceed- ings of the 51th Annual Meeting on Association for Com- putational Linguistics, pages 476-485. Citeseer, 2013.","title":"Leveraging synthetic discourse data via multi-task learning for implicit discourse relation recognition","authors":"Lan","year":"2013","venue":"Proceedings of the 51th Annual Meeting on Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lin et al., 2009] Ziheng Lin, Min-Yen Kan, and Hwee Tou Ng. Recognizing implicit discourse relations in the penn discourse treebank. In Proceedings of the 2009 Confer- ence on Empirical Methods in Natural Language Process- ing: Volume 1-Volume 1, pages 343-351. Association for Computational Linguistics, 2009.","title":"Recognizing implicit discourse relations in the penn discourse treebank","authors":"Lin","year":"2009","venue":"Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu and Li, 2016] Yang Liu and Sujian Li. Recognizing im- plicit discourse relations via repeated reading: Neural net- works with multi-level attention. EMNLP, 2016.","title":"Recognizing implicit discourse relations via repeated reading: Neural networks with multi-level attention","authors":"Li ; Liu, Yang Liu, Sujian Li","year":"2016","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2016] Yang Liu, Sujian Li, Xiaodong Zhang, and Zhifang Sui. Implicit discourse relation classification via multi-task neural networks. AAAI, 2016.","title":"Implicit discourse relation classification via multi-task neural networks","authors":"Yang Liu, Sujian Li, Xiaodong Zhang, Zhifang Sui","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Manning et al., 2014] Christopher D Manning, Mihai Sur- deanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. The stanford corenlp natural lan- guage processing toolkit. In ACL (System Demonstra- tions), pages 55-60, 2014.","title":"The stanford corenlp natural language processing toolkit","authors":"Mihai Christopher D Manning, John Surdeanu, Jenny Rose Bauer, Steven Finkel, David Bethard, Mcclosky","year":"2014","venue":"ACL (System Demonstrations)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Marcu and Echihabi, 2002a] Daniel Marcu and Ab- dessamad Echihabi. An unsupervised approach to recognizing discourse relations. In Proceedings of the 40th Annual Meeting on Association for Compu- tational Linguistics, pages 368-375. Association for Computational Linguistics, 2002.","title":"Daniel Marcu and Abdessamad Echihabi. An unsupervised approach to recognizing discourse relations","authors":"Echihabi Marcu","year":"2002","venue":"Proceedings of the 40th Annual Meeting on Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Marcu and Echihabi, 2002b] Daniel Marcu and Ab- dessamad Echihabi. An unsupervised approach to recognizing discourse relations. In Proceedings of the 40th Annual Meeting on Association for Compu- tational Linguistics, pages 368-375. Association for Computational Linguistics, 2002.","title":"Daniel Marcu and Abdessamad Echihabi. An unsupervised approach to recognizing discourse relations","authors":"Echihabi Marcu","year":"2002","venue":"Proceedings of the 40th Annual Meeting on Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In EMNLP, volume 14, pages 1532-43, 2014.","title":"Glove: Global vectors for word representation","authors":"Jeffrey Pennington, Richard Socher, Christopher D Manning","year":"2014","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pitler et al., 2009] Emily Pitler, Annie Louis, and Ani Nenkova. Automatic sense prediction for implicit dis- course relations in text. In Proceedings of the Joint Con- ference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, pages 683- 691. Association for Computational Linguistics, 2009.","title":"Automatic sense prediction for implicit discourse relations in text","authors":"Pitler","year":"2009","venue":"Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Prasad et al., 2008] Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Livio Robaldo, Aravind K Joshi, and Bonnie L Webber. The penn discourse treebank 2.0. In LREC. Citeseer, 2008.","title":"The penn discourse treebank 2.0","authors":"Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Livio Robaldo, Aravind K Joshi, Bonnie L Webber","year":"2008","venue":"LREC. Citeseer","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Rutherford and Xue, 2014] Attapol Rutherford and Nian- wen Xue. Discovering implicit discourse relations through brown cluster pair representation and coreference patterns. In EACL, volume 645, page 2014, 2014.","title":"Attapol Rutherford and Nianwen Xue. Discovering implicit discourse relations through brown cluster pair representation and coreference patterns","authors":"Xue Rutherford","year":"2014","venue":"EACL","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Rutherford and Xue, 2015] Attapol Rutherford and Nian- wen Xue. Improving the inference of implicit discourse relations via classifying explicit discourse connectives. In Proceedings of the NAACL-HLT, 2015.","title":"Attapol Rutherford and Nianwen Xue. Improving the inference of implicit discourse relations via classifying explicit discourse connectives","authors":"Xue Rutherford","year":"2015","venue":"Proceedings of the NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Schuster and Paliwal, 1997] Mike Schuster and Kuldip K Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673-2681, 1997.","title":"Bidirectional recurrent neural networks","authors":"Mike Schuster, Kuldip K Paliwal","year":"1997","venue":"IEEE Transactions on Signal Processing","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wieting et al., 2016] John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. Towards universal paraphras- tic sentence embeddings. ICLR, 2016.","title":"Towards universal paraphrastic sentence embeddings","authors":"John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu","year":"2016","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhang et al., 2016] Biao Zhang, Deyi Xiong, and Jinsong Su. Variational neural discourse relation recognizer. EMNLP, 2016.","title":"Variational neural discourse relation recognizer","authors":"Biao Zhang, Deyi Xiong, Jinsong Su","year":"2016","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhou et al., 2010] Zhi-Min Zhou, Yu Xu, Zheng-Yu Niu, Man Lan, Jian Su, and Chew Lim Tan. Predicting dis- course connectives for implicit discourse relation recogni- tion. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pages 1507-1514. Association for Computational Linguistics, 2010.","title":"Predicting discourse connectives for implicit discourse relation recognition","authors":"Zhou","year":"2010","venue":"Proceedings of the 23rd International Conference on Computational Linguistics: Posters","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}