research-article

Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting

Authors:

Sheng-Chieh Lin,

Jheng-Hong Yang,

Rodrigo Nogueira,

Ming-Feng Tsai,

Jimmy LinAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 39, Issue 4

Article No.: 48, Pages 1 - 29

https://doi.org/10.1145/3446426

Published: 01 September 2021 Publication History

Abstract

Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this article, we tackle conversational passage retrieval, an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a multi-stage ad hoc IR system. Specifically, we propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, stand-alone, human-understandable queries with a pretrained sequence-to-sequence model. Detailed analyses of the two CQR methods are provided quantitatively and qualitatively, explaining their advantages, disadvantages, and distinct behaviors. Moreover, to leverage the strengths of both CQR methods, we propose combining their output with reciprocal rank fusion, yielding state-of-the-art retrieval effectiveness, 30% improvement in terms of NDCG@3 compared to the best submission of Text REtrieval Conference (TREC) Conversational Assistant Track (CAsT) 2019.

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Cited By

Acharya PJones GFu XLipani ACrestani FKando NSakai TIshita EOhshima HHasibi FMao JJose J(2024)The 1st Workshop on User Modelling in Conversational Information Retrieval (UM-CIR)Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698436(315-317)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698436
Breuer TSakai TIshita EOhshima HHasibi FMao JJose J(2024)Data Fusion of Synthetic Query Variants With Generative Large Language ModelsProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698423(274-279)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698423
Ye ZXie XAi QLiu YWang ZSu WZhang M(2024)Relevance Feedback with Brain SignalsACM Transactions on Information Systems10.1145/363787442:4(1-37)Online publication date: 9-Feb-2024
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Index Terms

Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting
1. Information systems
  1. Information retrieval

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 39, Issue 4

October 2021

482 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3477247

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 01 September 2021

Accepted: 01 December 2020

Revised: 01 December 2020

Received: 01 May 2020

Published in TOIS Volume 39, Issue 4

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Cited By

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https://dl.acm.org/doi/10.1145/3673791.3698436
Breuer TSakai TIshita EOhshima HHasibi FMao JJose J(2024)Data Fusion of Synthetic Query Variants With Generative Large Language ModelsProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698423(274-279)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698423
Ye ZXie XAi QLiu YWang ZSu WZhang M(2024)Relevance Feedback with Brain SignalsACM Transactions on Information Systems10.1145/363787442:4(1-37)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3637874
Kostric IBalog KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)A Surprisingly Simple yet Effective Multi-Query Rewriting Method for Conversational Passage RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657933(2271-2275)Online publication date: 10-Jul-2024
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Acharya PFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Towards Effective Modeling and Exploitation of Search and User Context in Conversational Information RetrievalProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3616005(5161-5164)Online publication date: 21-Oct-2023
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