19/10/2020

Learning to generate reformulation actions for scalable conversational query understanding

Zihan Xu, Jiangang Zhu, Ling Geng, Yang Yang, Bojia Lin, Daxin Jiang

Keywords: contextual query reformulation, question answering, conversational query understanding

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Abstract: The ability of conversational query understanding (CQU) is indispensable to multi-turn QA. However, existing methods are data-driven and expensive to extend to new conversation domains, or under specific frameworks and hard to apply to other underlying QA technologies. We propose a novel contextual query reformulation (CQR) module based on reformulation actions for general CQU. The actions are domain-independent and scalable, since they capture syntactic regularities of conversations. For action generation, we propose a multi-task learning framework enhanced by coreference resolution, and introduce grammar constraints into the decoding process. Then CQR synthesizes standalone queries based on the actions, which naturally adapts to original downstream technologies. Experiments on different CQU datasets suggest that action-based methods substantially outperform direct reformulation, and the proposed model performs the best among the methods.

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