IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance

Yunah Jang, Kang-il Lee, Hyunkyung Bae, Hwanhee Lee, Kyomin Jung


Abstract
Conversational search aims to retrieve passages containing essential information to answer queries in a multi-turn conversation. In conversational search, reformulating context-dependent conversational queries into stand-alone forms is imperative to effectively utilize off-the-shelf retrievers. Previous methodologies for conversational query reformulation frequently depend on human-annotated rewrites.However, these manually crafted queries often result in sub-optimal retrieval performance and require high collection costs.To address these challenges, we propose **Iter**ative **C**onversational **Q**uery **R**eformulation (**IterCQR**), a methodology that conducts query reformulation without relying on human rewrites. IterCQR iteratively trains the conversational query reformulation (CQR) model by directly leveraging information retrieval (IR) signals as a reward.Our IterCQR training guides the CQR model such that generated queries contain necessary information from the previous dialogue context.Our proposed method shows state-of-the-art performance on two widely-used datasets, demonstrating its effectiveness on both sparse and dense retrievers. Moreover, IterCQR exhibits superior performance in challenging settings such as generalization on unseen datasets and low-resource scenarios.
Anthology ID:
2024.naacl-long.449
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8114–8131
Language:
URL:
https://aclanthology.org/2024.naacl-long.449
DOI:
Bibkey:
Cite (ACL):
Yunah Jang, Kang-il Lee, Hyunkyung Bae, Hwanhee Lee, and Kyomin Jung. 2024. IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 8114–8131, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance (Jang et al., NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-long.449.pdf
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