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• W2964053384 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-to-end 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." @default.
• W2964053384 created "2019-07-30" @default.
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• W2964053384 date "2017-01-01" @default.
• W2964053384 modified "2023-10-12" @default.
• W2964053384 title "Learning to Paraphrase for Question Answering" @default.
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• W2964053384 doi "https://doi.org/10.18653/v1/d17-1091" @default.
• W2964053384 hasPublicationYear "2017" @default.
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