EmotionIC：用于对话情感识别的情绪惯性与情绪传染驱动的依赖建模
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation
AI总结：
提出EmotionIC模型，通过IMMHA、DiaGRU和SkipCRF分别建模全局依赖、局部上下文和复杂情感流，在四个基准数据集上显著优于现有方法，有效建模情绪惯性与传染。
摘要：
对话情感识别（ERC）近年来随着人机交互界面技术的进步与实施而受到越来越多的关注。本文中，我们提出了一种用于ERC任务的情绪惯性与情绪传染驱动的依赖建模方法（EmotionIC）。我们的EmotionIC由三个主要组件组成，即身份掩蔽多头注意力（IMMHA）、基于对话的门控循环单元（DiaGRU）和跳跃链条件随机场（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更全面地建模对话。所提出的模型试图在特征提取层面整合基于注意力和基于递归的方法的优势。具体而言，IMMHA用于捕获基于身份的全局上下文依赖，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能够显式地从对话中高阶相邻话语中挖掘复杂的情感流。实验结果表明，我们的方法在四个基准数据集上显著优于最先进的模型。消融研究证实，我们的模块能够有效建模情绪惯性和情绪传染。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
