Reasoning vs surface-cue detection in emotion understanding models

Determine whether natural language processing models for emotion understanding genuinely perform reasoning about emotional states in textual inputs or primarily detect surface-level affective cues during multi-label emotion classification.

Background

The paper argues that many existing emotion benchmarks rely on short or decontextualized text and treat emotions as independent categories, which can obscure whether models are engaging in genuine reasoning about emotional states or merely picking up on superficial linguistic signals.

This ambiguity motivates the creation of EmoScene and the proposed entanglement-aware Bayesian inference framework, but the broader question of whether models truly reason about emotions versus detecting surface cues remains explicitly identified as unresolved.