利用情绪惯性提升对话情感识别准确率
EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation
- 在时间窗口内构建受情绪惯性影响的样本，指导对比学习
- 在IEMOCAP和MELD数据集上超越现有最佳方法
- 无需额外数据，可无缝集成到现有对话情感模型中
对话中的多模态情感识别（MERC）通过融合多模态与上下文信息实现精准预测。尽管现有方法关注对话中复杂的上下文依赖关系，却常忽略情绪转变时的上下文情绪惯性影响，导致性能受限。为此，我们提出一种新型情感惯性感知监督对比学习模块（EII-SCL），通过在时间窗口内构建受情绪惯性影响的样本，将情绪惯性作为先验信息融入对比目标，有效利用其特征，同时无需额外数据即可与现有MERC模型无缝集成。在IEMOCAP和MELD数据集上的大量实验表明，该方法持续优于当前最优方法。
原文摘要 · Abstract (English)
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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