发表机构
Huazhong University of Science and Technology（华中科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有情感基准缺乏情感相关要素结构化说明的问题，本文构建了以情感立场为核心的CUE Bench基准，实验证实融入情感立场可提升细粒度情感识别与语用意图检测性能。
AI 中文摘要
话语中的情感理解需要超越表层情感的推理，因为说话者常通过间接、隐含、礼貌、讽刺或刻意错位的表达传递情感。现有情感基准主要标注表层极性或最终情感类别，却缺乏对显性表达、隐含情感、语用意图与细粒度情感之间如何相互作用的结构化说明。这一局限导致当前评估对情感意义被隐藏、弱化、反转或语用重塑的案例不敏感，从而掩盖了模型在更深层次情感理解上的缺陷。为解决这一缺口，本文引入CUE Bench（即Chinese Unsaid Emotion基准），该基准以情感立场为核心，涵盖多样的交际场景。CUE Bench从显式-隐式极性互动中构建了9种可被人类解读的情感立场，并进一步提供意图与细粒度情感标注以支持结构化情感推理。实验结果表明，与强基线相比，融入情感立场可使细粒度情感识别性能提升3.5个百分点，语用意图检测性能提升7.8个百分点。
英文摘要
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.
Journal refEMNLP 2026 Main
