Conversational Query Reformulation with the Guidance of Retrieved Documents
This addresses the issue of improving conversational search for question answering systems, though it appears incremental as it builds on existing CQR methods.
The paper tackles the problem of conversational query reformulation (CQR) by introducing GuideCQR, a framework that refines queries using key information from initially retrieved documents, achieving state-of-the-art performance across multiple datasets.
Conversational search seeks to retrieve relevant passages for the given questions in conversational question answering. Conversational Query Reformulation (CQR) improves conversational search by refining the original queries into de-contextualized forms to resolve the issues in the original queries, such as omissions and coreferences. Previous CQR methods focus on imitating human written queries which may not always yield meaningful search results for the retriever. In this paper, we introduce GuideCQR, a framework that refines queries for CQR by leveraging key information from the initially retrieved documents. Specifically, GuideCQR extracts keywords and generates expected answers from the retrieved documents, then unifies them with the queries after filtering to add useful information that enhances the search process. Experimental results demonstrate that our proposed method achieves state-of-the-art performance across multiple datasets, outperforming previous CQR methods. Additionally, we show that GuideCQR can get additional performance gains in conversational search using various types of queries, even for queries written by humans.