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        <title>Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension</title>
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    <abstract>In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.</abstract>
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%0 Conference Proceedings
%T Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension
%A Li, Qian
%A Su, Hui
%A Niu, Cheng
%A Wang, Daling
%A Li, Zekang
%A Feng, Shi
%A Zhang, Yifei
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-etal-2019-answer
%X In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.
%R 10.18653/v1/D19-5805
%U https://aclanthology.org/D19-5805
%U https://doi.org/10.18653/v1/D19-5805
%P 38-47

Markdown (Informal)

[Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension](https://aclanthology.org/D19-5805) (Li et al., 2019)

• Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (Li et al., 2019)

ACL

• Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, and Yifei Zhang. 2019. Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 38–47, Hong Kong, China. Association for Computational Linguistics.