Year: 2017
Type: conference-paper
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.
DOI: 10.18653/v1/d17-1091
Authors: Li Dong, Jonathan Mallinson, Siva Reddy, Mirella Lapata
Institutions: Stanford University, University of Edinburgh
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2964053384