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Learning to Paraphrase for Question Answering

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arxiv 1708.06022 v1 pith:LAPVFOTF submitted 2017-08-20 cs.CL

Li Dong , Jonathan Mallinson , Siva Reddy , Mirella Lapata This is my paper · ORCID
classification cs.CL
keywords paraphrasesquestionansweringframeworkresultsachievingansweranswers
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation

    cs.CL 2019-08 conditional novelty 5.0 of 10

    DAgger-style imitation learning outperforms REINFORCE reinforcement learning for paraphrase generation with a pointer-generator, and the best model reaches state-of-the-art scores on Quora.

  2. Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System

    cs.IR 2019-08 conditional novelty 5.0 of 10

    QREFINE, a BERT- and character-aware Seq2Seq model trained with PPO and answer-aware rewards, generates cleaned questions that improve answer retrieval over previous refinement methods.

  3. Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE

    cs.AI 2025-09 reject novelty 4.0 of 10

    KG-SMILE applies perturbation and linear regression to a knowledge graph to attribute which entities and relations drive a GraphRAG system's answers.

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