ESIHGNN：融合事件-状态交互的异构图神经网络用于对话情感识别
ESIHGNN: Event-State Interactions Infused Heterogeneous Graph Neural Network for Conversational Emotion Recognition
AI总结：
本文提出ESIHGNN模型，通过构建异质事件-状态交互图并引入说话者情感状态和外部知识，动态建模对话，在四个CER数据集上验证了其优越性。
摘要：
对话情感识别（CER）旨在预测对话中某个话语（称为“事件”）所表达的情感。现有的基于图的方法主要关注事件交互以理解对话上下文，而忽视了说话者情感状态对事件的直接影响。此外，对话的实时建模对实际应用至关重要，但很少被考虑。为此，我们提出了一种新颖的基于图的方法，即融合事件-状态交互的异构图神经网络（ESIHGNN），该方法纳入说话者的情感状态，并构建异质的事件-状态交互图来建模对话。具体而言，采用异质有向无环图神经网络在每一轮动态更新和增强事件与情感状态的表示，从而提高对话的连贯性和一致性。此外，为了进一步提升CER的性能，我们用外部知识丰富图的边。在四个公开可用的CER数据集上的实验结果表明了我们方法的优越性以及所引入的异质事件-状态交互图的有效性。
英文摘要：
Conversational Emotion Recognition (CER) aims to predict the emotion expressed by an utterance (referred to as an ``event'') during a conversation. Existing graph-based methods mainly focus on event interactions to comprehend the conversational context, while overlooking the direct influence of the speaker's emotional state on the events. In addition, real-time modeling of the conversation is crucial for real-world applications but is rarely considered. Toward this end, we propose a novel graph-based approach, namely Event-State Interactions infused Heterogeneous Graph Neural Network (ESIHGNN), which incorporates the speaker's emotional state and constructs a heterogeneous event-state interaction graph to model the conversation. Specifically, a heterogeneous directed acyclic graph neural network is employed to dynamically update and enhance the representations of events and emotional states at each turn, thereby improving conversational coherence and consistency. Furthermore, to further improve the performance of CER, we enrich the graph's edges with external knowledge. Experimental results on four publicly available CER datasets show the superiority of our approach and the effectiveness of the introduced heterogeneous event-state interaction graph.
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。
