Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search

TL;DR

This paper introduces QRACDR, a model that improves conversational search by aligning query representations with rewritten queries and relevant documents, leading to better retrieval performance across multiple datasets.

Contribution

The paper proposes a novel query representation alignment method that leverages rewritten queries and relevance judgments to enhance conversational dense retrieval.

Findings

QRACDR outperforms state-of-the-art methods on eight datasets.

Representation alignment significantly improves retrieval accuracy.

The approach effectively utilizes both rewritten queries and relevance judgments.

Abstract

Conversational search supports multi-turn user-system interactions to solve complex information needs. Different from the traditional single-turn ad-hoc search, conversational search encounters a more challenging problem of context-dependent query understanding with the lengthy and long-tail conversational history context. While conversational query rewriting methods leverage explicit rewritten queries to train a rewriting model to transform the context-dependent query into a stand-stone search query, this is usually done without considering the quality of search results. Conversational dense retrieval methods use fine-tuning to improve a pre-trained ad-hoc query encoder, but they are limited by the conversational search data available for training. In this paper, we leverage both rewritten queries and relevance judgments in the conversational search data to train a better query representation model. The key idea is to align the query representation with those of rewritten queries and relevant documents. The proposed model -- Query Representation Alignment Conversational Dense Retriever, QRACDR, is tested on eight datasets, including various settings in conversational search and ad-hoc search. The results demonstrate the strong performance of QRACDR compared with state-of-the-art methods, and confirm the effectiveness of representation alignment.