CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation

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.

Authors

• Rui Ke Shenzhen Research Institute of Big Data, The School of Data Science, The Chinese University of Hong Kong, Shenzhen
• Jiahui Xu Zhejiang University
• Shenghao Yang Shenzhen Research Institute of Big Data, The School of Data Science, The Chinese University of Hong Kong, Shenzhen
• Kuang Wang Shenzhen Research Institute of Big Data, The School of Data Science, The Chinese University of Hong Kong, Shenzhen
• Feng Jiang The Chinese University of Hong Kong, Shenzhen Shenzhen Research Institute of Big Data University of Science and Technology of China
• Haizhou Li The Chinese University of Hong Kong, Shenzhen National University of Singapore Shenzhen Research Institute of Big Data

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