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.

我们提出了一种新颖的基于图的方法，即事件 - 状态交互注入异构图神经网络 (ESIHGNN)，它结合了说话者的情绪状态并构建了一个异构的事件 - 状态相互作用图来建模对话，通过动态更新和增强每个轮次中事件和情绪状态的表示来改善对话的连贯性和一致性。此外，为了进一步提高情感识别性能，我们利用外部知识丰富了图的边缘。在四个公开可用的情感识别数据集上的实验结果表明了我们方法的优越性以及引入的异构事件 - 状态相互作用图的有效性。