DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
This work addresses emotion recognition in conversation, which has applications in areas like health-care and education, but it appears incremental as it builds on existing graph neural network techniques for a specific task.
The paper tackles emotion recognition in conversation by proposing DialogueGCN, a graph neural network approach that models self and inter-speaker dependencies to address context propagation issues in RNN-based methods, and it outperforms state-of-the-art methods on benchmark datasets.
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.