Learning to Paraphrase for Question Answering

Li Dong, Jonathan Mallinson, Siva Reddy, Maria Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-toend using question-answer pairs as a supervision signal. A question and its paraphrases serve as input to a neural scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers. We evaluate our approach on QA over Freebase and answer sentence selection. Experimental results on three datasets show that our framework consistently improves performance, achieving competitive results despite the use of simple QA models.
Original languageEnglish
Title of host publicationProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics
Pages886–897
Number of pages12
ISBN (Print)978-1-945626-97-5
DOIs
Publication statusPublished - 11 Sep 2017
EventEMNLP 2017: Conference on Empirical Methods in Natural Language Processing - Copenhagen, Denmark
Duration: 7 Sep 201711 Sep 2017
http://emnlp2017.net/index.html
http://emnlp2017.net/

Conference

ConferenceEMNLP 2017: Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2017
Country/TerritoryDenmark
CityCopenhagen
Period7/09/1711/09/17
Internet address

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