  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.
