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 an emotional inertia and contagion-driven dependency modeling approach (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：对话情感识别（ERC）近年来因人机交互技术的进步与应用而受到广泛关注。本文提出了一种情感惯性与传染驱动的依赖建模方法（EmotionIC）用于ERC任务。EmotionIC由三个主要组件构成：身份掩码多头注意力机制（IMMHA）、基于对话的门控循环单元（DiaGRU）以及跳链条件随机场（SkipCRF）。与以往ERC模型相比，EmotionIC能够在特征提取与分类两个层面上更全面地建模对话过程。该模型旨在融合特征提取层面基于注意力与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语境依赖，而DiaGRU则用于提取说话者感知与时间感知的局部语境信息。在分类层面，SkipCRF能够从对话中高阶相邻话语中显式挖掘复杂的情感流。实验结果表明，该方法在四个基准数据集上显著优于当前最优模型。消融研究进一步证实，我们的模块能够有效建模情感惯性与传染现象。