提出CATCH框架，让对话主题识别更懂用户偏好和上下文。
CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation
- 用上下文增强语句语义，提升短对话的主题表达
- 结合用户反馈与语义相似度，实现跨对话主题对齐
- 分层生成机制有效降低噪声，提升主题标签质量
主题检测是用户中心对话系统中的基础任务，旨在不依赖预定义标签的情况下识别每句话的潜在话题。与固定标签空间下的意图识别不同，主题检测需保证跨对话一致性并贴合个性化用户偏好，面临巨大挑战。现有方法难以准确表示稀疏短文本的主题，且无法捕捉用户层面的主题偏好。为此，我们提出CATCH（可控制的主题检测框架），整合三个核心组件：(1) 上下文感知的主题表征，利用前后话题段落丰富语句语义；(2) 偏好引导的主题聚类，联合建模语义相近性与个性化反馈，实现对话间主题对齐；(3) 分层主题生成机制，抑制噪声，生成稳健连贯的主题标签。在多领域客户对话基准DSTC-12上，使用8B大语言模型验证了CATCH在主题聚类与主题生成质量上的有效性。
原文摘要 · Abstract (English)
Theme detection is a fundamental task in user-centric dialogue systems, aiming to identify the latent topic of each utterance without relying on predefined schemas. Unlike intent induction, which operates within fixed label spaces, theme detection requires cross-dialogue consistency and alignment with personalized user preferences, posing significant challenges. Existing methods often struggle with sparse, short utterances for accurate topic representation and fail to capture user-level thematic preferences across dialogues. To address these challenges, we propose CATCH (Controllable Theme Detection with Contextualized Clustering and Hierarchical Generation), a unified framework that integrates three core components: (1) context-aware topic representation, which enriches utterance-level semantics using surrounding topic segments; (2) preference-guided topic clustering, which jointly models semantic proximity and personalized feedback to align themes across dialogue; and (3) a hierarchical theme generation mechanism designed to suppress noise and produce robust, coherent topic labels. Experiments on a multi-domain customer dialogue benchmark (DSTC-12) demonstrate the effectiveness of CATCH with 8B LLM in both theme clustering and topic generation quality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。