Knowledge-Aware Graph Convolutional Network with Utterance-Specific Window Search for Emotion Recognition in Conversations

Emotion recognition in conversation (ERC) enables a deeper understanding of emotion for each utterance within a conversation. Recent progress on ERC has proved that using Graph Neural Networks (GNN) to model conversational context is effective for identifying emotions. However, existing GNN-based approaches still suffer from two limitations: (1) they model the context of each utterance with a certain window, which ignores the diversity of emotion changes of utterances in conversation; (2) they mostly take no account of additional knowledge information, which limits the performance of ERC. In this paper, we propose a knowledge-aware graph convolutional network (KGCN-ERC) by introducing a knowledge graph into node connection of graph neural networks for the first time. Based on the rich sentiment knowledge, KGCN-ERC searches for the most appropriate local window for each utterance and builds sensible utterance connections. Experiments show that our approach achieves competitive performance compared with state-of-the-art ERC methods.