[2603.03327] A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction

Computer Science > Computation and Language arXiv:2603.03327 (cs) [Submitted on 10 Feb 2026] Title:A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction Authors:Jing Bian, Haoxiang Su, Liting Jiang, Di Wu, Ruiyu Fang, Xiaomeng Huang, Yanbing Li, Shuangyong Song, Hao Huang View a PDF of the paper titled A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction, by Jing Bian and 8 other authors View PDF HTML (experimental) Abstract:User satisfaction is closely related to enterprises, as it not only directly reflects users' subjective evaluation of service quality or products, but also affects customer loyalty and long-term business revenue. Monitoring and understanding user emotions during interactions helps predict and improve satisfaction. However, relevant Chinese datasets are limited, and user emotions are dynamic; relying on single-turn dialogue cannot fully track emotional changes across multiple turns, which may affect satisfaction prediction. To address this, we constructed a multi-task, multi-label Chinese dialogue dataset that supports satisfaction recognition, as well as emotion recognition and emotional state transition prediction, providing new resources for studying emotion and satisfaction in dialogue systems. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.03327 [cs.CL]   (or arXiv:2603.03327v1 [cs.CL] for this ve...