@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://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.54/",
    pages = "662--671",
    ISBN = "979-8-89176-194-0",
    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."
}