Emotion Recognition in Conversation (ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper, we propose a novel approach to dependency modeling driven by Emotional Inertia and Contagion (EmotionIC) for ERC task. Our EmotionIC consists of three main components, i.e., Identity Masked Multi-Head Attention (IMMHA), Dialogue-based Gated Recurrent Unit (DiaGRU), and Skip-chain Conditional Random Field (SkipCRF). Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention- and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while DiaGRU is utilized to extract speaker- and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets. The ablation studies confirm that our modules can effectively model emotional inertia and contagion.

翻译：摘要：对话情感识别（ERC）近年来随着人机交互技术的进步与应用而受到越来越多的关注。本文提出了一种由情感惯性与传染驱动的依赖建模新方法（EmotionIC），用于ERC任务。我们的EmotionIC包含三个主要组件：身份掩码多头注意力机制（IMMHA）、基于对话的门控循环单元（DiaGRU）和跳链条件随机场（SkipCRF）。与以往的ERC模型相比，EmotionIC能够在特征提取和分类两个层面对对话进行更全面的建模。该模型尝试在特征提取层面融合基于注意力机制和基于循环方法的优势。具体而言，IMMHA用于捕获基于身份的全局上下文依赖关系，而DiaGRU用于提取说话者感知和时间感知的局部上下文信息。在分类层面，SkipCRF能够从对话中高阶相邻语句中显式挖掘复杂的情感流。实验结果表明，我们的方法在四个基准数据集上显著优于当前最先进的模型。消融研究证实，我们的模块能够有效建模情感惯性与传染。