Rationale-Guided Learning for Multimodal Emotion Recognition

Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. However, most existing approaches fundamentally treat this as a direct input-output (multimodal cues-emotion labels) mapping problem, overlooking the causal reasoning that humans use when interpreting emotions. We propose rationale-guided learning (RGL), a novel framework that transforms MERC into a cognitively-inspired reasoning task. Based on dual-process theory

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ethics.ai (11 August 2026), “Rationale-Guided Learning for Multimodal Emotion Recognition,” evidence record 18427, https://ethics.ai/record/18427 (originally published by arXiv).

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