Position-aware Graph Attention Networks for Emotion Recognition in Conversations

Abstract

Interest in emotion recognition in conversations (ERC) has been increasing in various fields, because it can be used in the various tasks such as analyzing user behaviors and detecting fake news. Many recent ERC methods use graph neural networks to consider the relationships between the utterances of the speakers. In particular, the strong method considers self-speaker and inter-speaker dependencies in conversations by using relational graph attention networks (RGAT). However, graph neural networks do not consider sequential information. In this paper, we propose relational position encodings that provide RGAT with sequential information reflecting the relational graph structure. Therefore, our RGAT model can capture both the speaker dependency and the sequential information. Experiments on three ERC datasets show that our model is beneficial to recognizing emotions expressed in conversations. In addition, our approach empirically outperforms the state-of-the-art on several benchmark datasets.