Chiaroscuro for Emotions: A Contrastive Emotion Benchmark Grounded in Appraisal Theory

Abstract:Emotion recognition benchmarks often predict one emotion per text, missing many real-world scenarios where two people arrive at opposing emotions from a single shared event. For example, a child kicks the seat in front of her in excitement while the passenger ahead grows angry. We introduce CHIARO, a 1,000 human-annotated sentence benchmark for contrastive emotion inference grounded in appraisal theory. Each scene describes one causal trigger eliciting a positive emotion in one person and a negative emotion in the other, drawn from a ten-class taxonomy. We benchmark seven frontier LLMs and four off-the-shelf emotion classifiers. The strongest LLM reaches 67.3 macro-F1, well below human agreement, while existing emotion classifiers score near chance. Beyond evaluation, CHIARO also serves as a training signal. When combined with an existing emotion corpus, the resulting downstream classifier improves on CHIARO itself and on six of ten external emotion benchmarks, which positions our dataset as a complementary signal for emotion recognition.

Computation and Language (cs.CL)

arXiv:2609.03394 [cs.CL]

(or arXiv:2609.03394v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.03394 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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