构建中文情感隐含表达评测集，提升模型对隐性情绪的理解能力
Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse
- 提出九种可解释的情感立场分类，融合显性与隐性情绪互动
- 在细粒度情绪识别上提升3.1个百分点，语用意图识别提升8.1个百分点
- 适合研究中文隐含情绪、语用推理与情感计算的学者使用
话语中的情绪理解需要超越表面情感的推理，因为说话人常通过间接、隐含、礼貌、讽刺或故意错配的方式传达情感。现有情绪评测集主要标注表面极性或最终情绪类别，缺乏对显性表达、隐性情感、语用意图及细粒度情绪之间交互关系的系统刻画。这一局限导致现有评估对情感意义被隐藏、弱化、反转或语用重构的情况不敏感，掩盖了模型在深层情绪理解上的缺陷。为此，我们提出CUE Bench，一个聚焦中文语境下情感立场的无言情绪评测基准，涵盖多样交际场景。CUE Bench基于显性与隐性极性交互，构建九类人类可解释的情感立场，并提供语用意图与细粒度情绪标注，支持结构化情感推断。实验表明，引入情感立场使细粒度情绪识别准确率提升3.1个百分点，语用意图检测提升8.1个百分点，优于强基线模型。
原文摘要 · Abstract (English)
Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite, ironic, or deliberately mismatched expressions. Existing emotion benchmarks mainly annotate surface polarity or final emotion categories, while lacking a structured account of how explicit expression, implicit affect, pragmatic intent, and fine grained emotion interact. This limitation makes current evaluations insensitive to cases where affective meaning is concealed, weakened, inverted, or pragmatically reshaped, thereby obscuring model failures in deeper emotion understanding. To address this gap, we introduce CUE Bench, a Chinese Unsaid Emotion benchmark that centers on Affective Stance and covers diverse communicative scenarios. CUE Bench constructs nine human interpretable affective stances from explicit implicit polarity interaction and further provides intent and fine grained emotion annotations for structured affective inference. Experiments show that incorporating Affective Stance improves fine grained emotion recognition by 3.1 percentage points and pragmatic intent detection by 8.1 percentage points over strong baselines.
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