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        <title>Explicit Query Rewriting for Conversational Dense Retrieval</title>
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    <abstract>In a conversational search scenario, a query might be context-dependent because some words are referred to previous expressions or omitted. Previous works tackle the issue by either reformulating the query into a self-contained query (query rewriting) or learning a contextualized query embedding from the query context (context modelling). In this paper, we propose a model CRDR that can perform query rewriting and context modelling in a unified framework in which the query rewriting’s supervision signals further enhance the context modelling. Instead of generating a new query, CRDR only performs necessary modifications on the original query, which improves both accuracy and efficiency of query rewriting. In the meantime, the query rewriting benefits the context modelling by explicitly highlighting relevant terms in the query context, which improves the quality of the learned contextualized query embedding. To verify the effectiveness of CRDR, we perform comprehensive experiments on TREC CAsT-19 and TREC CAsT-20 datasets, and the results show that our method outperforms all baseline models in terms of both quality of query rewriting and quality of context-aware ranking.</abstract>
    <identifier type="citekey">qian-dou-2022-explicit</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-main.311</identifier>
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%0 Conference Proceedings
%T Explicit Query Rewriting for Conversational Dense Retrieval
%A Qian, Hongjin
%A Dou, Zhicheng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F qian-dou-2022-explicit
%X In a conversational search scenario, a query might be context-dependent because some words are referred to previous expressions or omitted. Previous works tackle the issue by either reformulating the query into a self-contained query (query rewriting) or learning a contextualized query embedding from the query context (context modelling). In this paper, we propose a model CRDR that can perform query rewriting and context modelling in a unified framework in which the query rewriting’s supervision signals further enhance the context modelling. Instead of generating a new query, CRDR only performs necessary modifications on the original query, which improves both accuracy and efficiency of query rewriting. In the meantime, the query rewriting benefits the context modelling by explicitly highlighting relevant terms in the query context, which improves the quality of the learned contextualized query embedding. To verify the effectiveness of CRDR, we perform comprehensive experiments on TREC CAsT-19 and TREC CAsT-20 datasets, and the results show that our method outperforms all baseline models in terms of both quality of query rewriting and quality of context-aware ranking.
%R 10.18653/v1/2022.emnlp-main.311
%U https://aclanthology.org/2022.emnlp-main.311
%U https://doi.org/10.18653/v1/2022.emnlp-main.311
%P 4725-4737

Markdown (Informal)

[Explicit Query Rewriting for Conversational Dense Retrieval](https://aclanthology.org/2022.emnlp-main.311) (Qian & Dou, EMNLP 2022)

• Explicit Query Rewriting for Conversational Dense Retrieval (Qian & Dou, EMNLP 2022)

ACL

• Hongjin Qian and Zhicheng Dou. 2022. Explicit Query Rewriting for Conversational Dense Retrieval. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4725–4737, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.