构建中文多任务对话数据集，支持满意度与情绪动态预测。
A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction
- 构建多任务多标签中文对话数据集，支持满意度、情绪识别与状态转移。
- 首个支持情绪状态跨轮次动态追踪的中文对话评估基准。
- 适合研究对话系统情感计算与用户体验优化的研究者使用。
用户满意度与企业密切相关，不仅反映服务或产品体验，还影响客户忠诚度和长期营收。在交互过程中监测和理解用户情绪有助于预测和提升满意度。然而，现有中文数据集有限，且用户情绪具有动态性，仅依赖单轮对话无法完整追踪多轮次中的情绪变化，可能影响满意度预测效果。为此，我们构建了一个多任务、多标签的中文对话数据集，支持满意度识别、情绪识别以及情绪状态转移预测，为研究对话系统中的情绪与满意度提供了新资源。
原文摘要 · Abstract (English)
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.
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