EII-SCL：利用情感惯性进行对话中的多模态情感识别
EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation
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中文总结 AI 辅助
研究对话中的多模态情感识别问题，提出情感惯性引导监督对比学习模块EII-SCL，通过构建受惯性影响样本为对比目标提供信息，利用情感惯性先验，无需额外数据，与现有模型无缝集成，实验证明该方法优于现有方法。
中文摘要 AI 辅助
对话中的多模态情感识别（MERC）通过整合对话中的多模态和上下文信息来实现准确预测。当前MERC方法专注于对对话中的复杂上下文依赖进行建模，却常忽视上下文情感惯性在情感转变中的影响，导致性能欠佳。为解决此问题，我们提出了一种新颖的情感惯性引导监督对比学习模块（EII-SCL），通过在时间窗口内构建受惯性影响的样本为对比目标提供信息，有效利用情感惯性作为先验，且无需额外数据就能与现有MERC模型无缝集成。在IEMOCAP和MELD上的大量实验表明，我们的方法始终优于现有方法。
英文摘要
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.
发表机构
- Electronic Engineering,\ Hong Kong Polytechnic University, Hong Kong SAR, China
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