Query Variant Detection Using Retriever as Environment
Minji Seo, Youngwon Lee, Seung-won Hwang, Seoho Song, Hee-Cheol Seo, Young-In Song
Abstract
This paper addresses the challenge of detecting query variants—pairs of queries with identical intents. One application in commercial search engines is reformulating user queries with its variant online. While measuring pairwise query similarity has been an established standard, it often falls short of capturing semantic equivalence when word forms or order differ. We propose leveraging the retrieval as an environment feedback (EF), based on the premise that desirable retrieval outcomes from equivalent queries should be interchangeable. Experimental results on both proprietary and public datasets demonstrate the efficacy of the proposed method, both with and without LLM calls.
BibTeX
@inproceedings{seo-etal-2025-query,
title = "Query Variant Detection Using Retriever as Environment",
author = "Seo, Minji and
Lee, Youngwon and
Hwang, Seung-won and
Song, Seoho and
Seo, Hee-Cheol and
Song, Young-In",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.54/",
pages = "662--671",
ISBN = "979-8-89176-194-0"
}