CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning

• URL: http://arxiv.org/abs/2112.08558v1
• Date: Thu, 16 Dec 2021 01:40:30 GMT
• Title: CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
• Authors: Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Gaurav Singh Tomar
• Abstract summary: We develop a query rewriting model CONQRR that rewrites a conversational question in context into a standalone question. We show that CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset.
• Score: 16.470428531658232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For open-domain conversational question answering (CQA), it is important to retrieve the most relevant passages to answer a question, but this is challenging compared with standard passage retrieval because it requires understanding the full dialogue context rather than a single query. Moreover, it can be expensive to re-train well-established retrievers such as search engines that are originally developed for non-conversational queries. To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in context into a standalone question. It is trained with a novel reward function to directly optimize towards retrieval and can be adapted to any fixed blackbox retriever using reinforcement learning. We show that CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset, a combination of conversations from three different sources. We also conduct extensive experiments to show the effectiveness of CONQRR for any given fixed retriever.

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