{"type":"ScholarlyArticle","title":"Finding Prototypes of Answers for Improving Answer Sentence Selection","authors":[{"name":"Wai Lok Tam","slug":"wai-lok-tam","given":"Wai Lok","family":"Tam","affiliation":"National Institute of Informatics , Japan"},{"name":"Namgi Han","slug":"namgi-han","given":"Namgi","family":"Han","affiliation":"National Institute of Informatics , Japan; The Graduate University for Advanced Studies , Japan"},{"name":"Juan Ignacio Navarro-Horñiacek","slug":"juan-ignacio-navarro-horniacek","given":"Juan Ignacio","family":"Navarro-Horñiacek","affiliation":"National Institute of Informatics , Japan; The Graduate University for Advanced Studies , Japan; Livesense Inc. , Japan; Institution for a Global Society , Japan."},{"name":"Yusuke Miyao","slug":"yusuke-miyao","given":"Yusuke","family":"Miyao","affiliation":"National Institute of Informatics , Japan; The Graduate University for Advanced Studies , Japan"}],"abstract":"Answer sentence selection has been widely adopted recently for benchmarking techniques in Question Answering. Previous proposals for the task are essentially general solutions taking the form of neural networks that measure semantic similarity.  In contrast, the present paper describes a simple technique to take advantage of such general-purpose tools for dealing with questions and answer sentences without changing the base system. The technique involves replacing wh-words in input questions with a word denoting the prototype of all answers.  These transformed questions are passed as input to an existing neural network built for measuring semantic similarity. This technique is evaluated on two different neural network architectures over two datasets: TrecQA and WikiQA. Results of our experiments show improvement in overall accuracy across most question types we are interested in: `who', `when' and `where'-type questions.","doi":"10.24963/ijcai.2017/573","url":"https://ijcai.org/proceedings/2017/573","pdf":"https://ijcai.org/proceedings/2017/0573.pdf","markdown":"https://ijcai.org/proceedings/2017/573.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/573","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":573,"pages":{"first":4103,"last":4108},"keywords":["Natural Language Processing: Question Answering","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[dos Santos et al., 2016] Cícero Nogueira dos Santos, Ming Tan, Bing Xiang, and Bowen Zhou. Attentive pooling net- works. CoRR, abs/1602.03609, 2016.","title":"Attentive pooling networks","authors":"Cícero Nogueira Dos Santos, Ming Tan, Bing Xiang, Bowen Zhou","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Feng et al., 2015] Minwei Feng, Bing Xiang, Michael R. Glass, Lidan Wang, and Bowen Zhou. Applying deep learning to answer selection: A study and an open task. CoRR, abs/1508.01585, 2015.","title":"Applying deep learning to answer selection: A study and an open task","authors":"Feng","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Finkel et al., 2005] Jenny Rose Finkel, Trond Grenager, and Christopher Manning. Incorporating non-local informa- tion into information extraction systems by gibbs sam- pling. In Proceedings of the 43rd Annual Meeting on As- sociation for Computational Linguistics, ACL '05, pages 363-370, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics.","title":"Incorporating non-local information into information extraction systems by gibbs sampling","authors":"Jenny Rose Finkel, Trond Grenager, Christopher Manning","year":"2005","venue":"Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL '05","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[He and Lin, 2016] Hua He and Jimmy J. Lin. Pairwise word interaction modeling with deep neural networks for se- mantic similarity measurement. In NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lan- guage Technologies, San Diego California, USA, June 12- 17, 2016, pages 937-948, 2016.","title":"Pairwise word interaction modeling with deep neural networks for semantic similarity measurement","authors":"Lin ; ] Hua He, Jimmy J He, Lin","year":"2016","venue":"The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[He et al., 2015] Hua He, Kevin Gimpel, and Jimmy Lin. Multi-perspective sentence similarity modeling with con- volutional neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1576-1586, Lisbon, Portugal, Septem- ber 2015. Association for Computational Linguistics.","title":"Multi-perspective sentence similarity modeling with convolutional neural networks","authors":"Hua He, Kevin Gimpel, Jimmy Lin","year":"2015","venue":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Comput., 9(8):1735-1780, November 1997.","title":"Long short-term memory","authors":"Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural Comput","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Krizhevsky et al., 2012] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Ad- vances in Neural Information Processing Systems 25, pages 1097-1105.","title":"Imagenet classification with deep convolutional neural networks","authors":"Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton","year":null,"venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"Curran Associates, Inc., 2012.","title":null,"authors":"Curran Associates, Inc","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Lakoff, 1987] George Lakoff. Women, Fire and Dangerous Things. University of Chicago Press, 1987.","title":"Women, Fire and Dangerous Things","authors":"George Lakoff","year":"1987","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li and Roth, 2005] X. Li and D. Roth. The role of semantic information. Journal of Natural Language Engineering, 2005.","title":"The role of semantic information","authors":"Roth ; Li, X Li, D Roth","year":"2005","venue":"Journal of Natural Language Engineering","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In Empirical Meth- ods in Natural Language Processing (EMNLP), pages 1532-1543, 2014.","title":"Glove: Global vectors for word representation","authors":"Jeffrey Pennington, Richard Socher, Christopher D Manning","year":"2014","venue":"Empirical Methods in Natural Language Processing (EMNLP)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Rao et al., 2016] Jinfeng Rao, Hua He, and Jimmy Lin. Noise-contrastive estimation for answer selection with deep neural networks. In Proceedings of the 25th ACM In- ternational on Conference on Information and Knowledge Management, CIKM '16, pages 1913-1916, New York, NY, USA, 2016. ACM.","title":"Noise-contrastive estimation for answer selection with deep neural networks","authors":"Rao","year":"2016","venue":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM '16","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Rosch, 1973] Eleanor Rosch. Natural Categories. In Cogni- tive Psychology, volume 4, pages 328-350. Kluwer, 1973.","title":"Eleanor Rosch. Natural Categories","authors":"Rosch","year":"1973","venue":"Cognitive Psychology","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Tan et al., 2015] Ming Tan, Bing Xiang, and Bowen Zhou. Lstm-based deep learning models for non-factoid answer selection. CoRR, abs/1511.04108, 2015.","title":"Lstm-based deep learning models for non-factoid answer selection","authors":"Ming Tan, Bing Xiang, Bowen Zhou","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Wang et al., 2007] Mengqiu Wang, Noah A. Smith, and Teruko Mitamura. What is the jeopardy model? a quasi- synchronous grammar for qa. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Lan- guage Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 22-32, Prague, Czech Republic, June 2007. Association for Computational Lin- guistics.","title":"What is the jeopardy model? a quasisynchronous grammar for qa","authors":"Wang","year":"2007","venue":"Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Yang et al., 2016] Liu Yang, Qingyao Ai, Jiafeng Guo, and W. Bruce Croft. anmm: Ranking short answer texts with attention-based neural matching model. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM '16, pages 287-296, New York, NY, USA, 2016. ACM.","title":"anmm: Ranking short answer texts with attention-based neural matching model","authors":"Liu Yang, Qingyao Ai, Jiafeng Guo, W Bruce Croft","year":"2016","venue":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM '16","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Yi Yang, 2015] Chris Meek Yi Yang, Scott Wen-tau Yih. Wikiqa: A challenge dataset for open-domain question an- swering. In Proceedings of the 2015 Conference on Empir- ical Methods in Natural Language Processing. ACL As- sociation for Computational Linguistics, September 2015.","title":"Wikiqa: A challenge dataset for open-domain question answering","authors":"Chris Meek, Yi Yang, Scott, Wen-Tau Yih","year":"2015","venue":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. ACL Association for Computational Linguistics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}