@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T KAPA: A Deliberative Agent Framework with Tree-Structured Knowledge Base for Multi-Domain User Intent Understanding
%A Tang, Jiakai
%A Shen, Shiqi
%A ZhipengWang, ZhipengWang
%A Zhi, Gong
%A Feng, Xueyang
%A Sun, Zexu
%A Tan, Haoran
%A Chen, Xu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F tang-etal-2025-kapa
%X 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.
%R 10.18653/v1/2025.findings-acl.319
%U https://aclanthology.org/2025.findings-acl.319/
%U https://doi.org/10.18653/v1/2025.findings-acl.319
%P 6150-6166
Markdown (Informal)
[KAPA: A Deliberative Agent Framework with Tree-Structured Knowledge Base for Multi-Domain User Intent Understanding](https://aclanthology.org/2025.findings-acl.319/) (Tang et al., Findings 2025)
ACL
- Jiakai Tang, Shiqi Shen, ZhipengWang ZhipengWang, Gong Zhi, Xueyang Feng, Zexu Sun, Haoran Tan, and Xu Chen. 2025. KAPA: A Deliberative Agent Framework with Tree-Structured Knowledge Base for Multi-Domain User Intent Understanding. In Findings of the Association for Computational Linguistics: ACL 2025, pages 6150–6166, Vienna, Austria. Association for Computational Linguistics.