@inproceedings{tang-etal-2025-kapa,
    title = "{KAPA}: A Deliberative Agent Framework with Tree-Structured Knowledge Base for Multi-Domain User Intent Understanding",
    author = "Tang, Jiakai  and
      Shen, Shiqi  and
      ZhipengWang, ZhipengWang  and
      Zhi, Gong  and
      Feng, Xueyang  and
      Sun, Zexu  and
      Tan, Haoran  and
      Chen, Xu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.319/",
    doi = "10.18653/v1/2025.findings-acl.319",
    pages = "6150--6166",
    ISBN = "979-8-89176-256-5",
    abstract = "Dialogue assistants have become ubiquitous in modern applications, fundamentally reshaping human daily communication patterns and information access behaviors. In real-world conversational interactions, however, user queries are often volatile, ambiguous, and diverse, making it difficult accurately and efficiently grasp the user{'}s underlying intentions. To address this challenge, we propose a simple yet effective deliberative agent framework that leverages human thought process to build high-level domain knowledge. To further achieve efficient knowledge accumulation and retrieval, we design a tree-structured knowledge base to store refined experience and data. Moreover, we construct a new benchmark, User-Intent-Understanding (UIU), which covers multi-domain, multi-tone, and sequential multi-turn personalized user queries. Extensive experiments demonstrate the effectiveness of our proposed method across multi-step evaluations."
}