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

2026-09-03

AI Summary

论文针对单一事件引发不同情绪的问题，基于评价理论提出了包含1000句人类标注的对比情绪基准CHIARO，并通过实验展示了其在情绪识别中的有效性。

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.

Problem

• emotion recognition
• contrastive emotion
• appraisal theory

Innovation

• contrastive emotion inference
• appraisal theory
• benchmark
• human-annotated sentences
• emotional polarity