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Rationale-Guided Learning Boosts Multimodal Emotion Recognition

Sujung Oh, Jung Uk Kim, Sangmin Lee· August 12, 2026 View original

Key takeaways

  • Rationale-Guided Learning (RGL) improves multimodal emotion recognition by mimicking human reasoning.
  • It uses MLLMs to generate structured rationales offline, guiding model training.
  • The final RGL model operates efficiently without MLLM overhead during inference.
  • RGL achieves state-of-the-art performance on key emotion recognition benchmarks.

Who benefits

Customer ServiceHealthcareEdTechSocial MediaEntertainment

Summary

Researchers developed Rationale-Guided Learning (RGL), a novel framework that transforms multimodal emotion recognition into a cognitively-inspired reasoning task by leveraging MLLMs to generate structured rationales. This approach, based on dual-process theory, significantly improves performance on emotion recognition benchmarks without MLLM overhead during inference.

This paper introduces Rationale-Guided Learning (RGL), a new framework designed to improve Multimodal Emotion Recognition in Conversation (MERC). Traditional MERC models often treat the task as a direct mapping from multimodal cues to emotion labels, overlooking the complex causal reasoning humans employ. RGL addresses this by reframing MERC as a cognitively-inspired reasoning task. The framework draws on dual-process theory, decomposing emotional reasoning into intuitive (System 1), contextual (System 2), and integrative facets. It utilizes a Multimodal Large Language Model (MLLM) offline to generate structured rationales, which are then encoded as memories. These memories guide the training of the main model by aligning its internal representations with human-like reasoning patterns. A key advantage is that the final RGL model operates without any MLLM overhead during inference, making it efficient for deployment. Experimental results demonstrate that RGL achieves state-of-the-art performance on prominent MERC benchmarks like IEMOCAP and MELD. Furthermore, the model's internal features effectively retrieve semantically correct rationales for unseen samples, validating its enhanced reasoning capabilities.

Why it matters

For professionals in AI development, particularly those in customer experience, mental health tech, or human-computer interaction, this research offers a more robust and interpretable approach to emotion recognition, potentially leading to more empathetic and effective AI systems.

How to implement this in your domain

  1. 1Investigate integrating rationale-guided learning into existing multimodal AI systems for improved interpretability and performance.
  2. 2Explore using MLLMs for offline rationale generation to enhance training data for specific tasks.
  3. 3Apply dual-process theory principles to design more sophisticated AI reasoning frameworks.
  4. 4Evaluate the potential of RGL for applications requiring nuanced understanding of human emotions.

Original post by Sujung Oh, Jung Uk Kim, Sangmin Lee

"arXiv:2608.10448v1 Announce Type: new Abstract: 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 cu…"

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Originally posted by Sujung Oh, Jung Uk Kim, Sangmin Lee on X · view source

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