A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866910040014716928 |
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| author | Bian, Jing Su, Haoxiang Jiang, Liting Wu, Di Fang, Ruiyu Huang, Xiaomeng Li, Yanbing Song, Shuangyong Huang, Hao |
| author_facet | Bian, Jing Su, Haoxiang Jiang, Liting Wu, Di Fang, Ruiyu Huang, Xiaomeng Li, Yanbing Song, Shuangyong Huang, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03327 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction Bian, Jing Su, Haoxiang Jiang, Liting Wu, Di Fang, Ruiyu Huang, Xiaomeng Li, Yanbing Song, Shuangyong Huang, Hao Computation and Language Artificial Intelligence 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. |
| title | A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.03327 |