  Emotion recognition in conversation (ERC) has received much attention,
lately, from researchers due to its potential widespread applications in
diverse areas, such as health-care, education, and human resources. In this
paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph
neural network based approach to ERC. We leverage self and inter-speaker
dependency of the interlocutors to model conversational context for emotion
recognition. Through the graph network, DialogueGCN addresses context
propagation issues present in the current RNN-based methods. We empirically
show that this method alleviates such issues, while outperforming the current
state of the art on a number of benchmark emotion classification datasets.
