@inproceedings{abbasiantaeb-etal-2026-generating,
    title = "Generating Multi-Aspect Queries for Conversational Search",
    author = "Abbasiantaeb, Zahra  and
      Lupart, Simon  and
      Aliannejadi, Mohammad",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.383/",
    pages = "8201--8217",
    ISBN = "979-8-89176-380-7",
    abstract = "Conversational information seeking (CIS) systems aim to model the user{'}s information need within the conversational context and retrieve the relevant information. One major approach to modeling the conversational context aims to rewrite the user utterance in the conversation to represent the information need independently. In this work, we hypothesize that breaking down the information of an utterance into multiple queries covering different aspects of the information need can lead to more effective retrieval performance. This is more evident in more complex utterances that require gathering evidence from various information sources, where a single query rewrite or query representation cannot capture the complexity of the utterance. We propose MQ4CS, a multi-aspect query generation and retrieval framework, which uses Large Language Models (LLMs) to break the user utterance into multiple queries. This approach improves retrieval performance, as most utterances benefit from more than one rewritten query. We evaluate MQ4CS on six widely used CIS datasets, showing it outperforms state-of-the-art query rewriting methods. Using MQ4CS, we also construct MASQ, which includes multiple-aspect queries for the six datasets. Fine-tuning the model on MASQ yields significant improvements. We make our code and dataset publicly available."
}