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arXiv · 2607.17366

EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation

Zilong Huang·Kong Aik Lee·Chong-Xin Gan·Zezhong Jin·Ruichen Zuo·Man-Wai Mak
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Abstract

Multimodal emotion recognition in conversation (MERC) achieves accurate predictions by integrating multimodal and contextual information in dialogues. While current MERC approaches focus on modeling complex contextual dependencies in conversation, they often overlook the impact of contextual emotional inertia in emotion shift, leading to sub-optimal performance. To address this issue, we propose a novel Emotional Inertia-Informed Supervised Contrastive Learning module (EII-SCL) that informs the contrastive objective by constructing inertia-affected samples within temporal windows, effectively leveraging emotional inertia as a prior while enabling seamless integration with existing MERC models without requiring additional data. Extensive experiments on IEMOCAP and MELD show that our approach consistently outperforms state-of-the-art methods.

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Zilong Huang, Kong Aik Lee, Chong-Xin Gan, Zezhong Jin, Ruichen Zuo, Man-Wai Mak. 2026-07-19. EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation. https://arxiv.org/abs/2607.17366

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