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    <abstract>Emotion understanding in discourse requires reasoning beyond surface sentiment, since 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 models’ failures in deeper emotion understanding. To address this gap, we introduce **CUE-Bench**, a **C**hinese **U**nsaid **E**motion 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. Project page, data, and code are available at [https://ai4ss.github.io/CUE-Bench/](https://ai4ss.github.io/CUE-Bench/).</abstract>
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%0 Conference Proceedings
%T Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse
%A Zheng, Zhenyan
%A Zhang, Yunyao
%A Sheng, Junxi
%A Yu, Junqing
%A Song, Zikai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zheng-etal-2026-surfacing
%X Emotion understanding in discourse requires reasoning beyond surface sentiment, since 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 models’ failures in deeper emotion understanding. To address this gap, we introduce **CUE-Bench**, a **C**hinese **U**nsaid **E**motion 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. Project page, data, and code are available at [https://ai4ss.github.io/CUE-Bench/](https://ai4ss.github.io/CUE-Bench/).
%U https://aclanthology.org/2026.emnlp-main.1588/
%P 36109-36126

Markdown (Informal)

[Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse](https://aclanthology.org/2026.emnlp-main.1588/) (Zheng et al., EMNLP 2026)

• Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse (Zheng et al., EMNLP 2026)

ACL

• Zhenyan Zheng, Yunyao Zhang, Junxi Sheng, Junqing Yu, and Zikai Song. 2026. Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 36109–36126, Budapest, Hungary. Association for Computational Linguistics.