EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation
Incremental improvement for multimodal emotion recognition in conversation.
The paper tackles the problem of multimodal emotion recognition in conversation, where existing methods overlook emotional inertia. The proposed EII-SCL module improves performance by 1-2% over state-of-the-art on IEMOCAP and MELD datasets.
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.