aiqbench analysis
This research introduces EII-SCL, a novel module for Multimodal Emotion Recognition in Conversation (MERC) that explicitly models emotional inertia within temporal windows to improve prediction accuracy. By leveraging this psychological prior as a contrastive learning objective, the method outperforms state-of-the-art benchmarks on standard datasets like IEMOCAP and MELD without requiring additional training data. The work represents significant progress in applying advanced machine learning techniques to nuanced human-computer interaction tasks where contextual emotional dynamics are critical.
EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation
arXiv:2607.17366v1 Announce Type: cross 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.
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.
Multimedia (cs.MM); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Signal Processing (eess.SP)
arXiv:2607.17366 [cs.MM]
(or arXiv:2607.17366v1 [cs.MM] for this version)
https://doi.org/10.48550/arXiv.2607.17366 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 208 words
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