Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations

Abstract

Emotion recognition in multi-party conversations (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Recently, many approaches have been devoted to exploiting inter-dependency and self-dependency among participants. However, these approaches remain inadequate in terms of inter-dependency due to the fact that the effects among speakers are not individually captured. In this paper, we design two hypergraphs to deal with inter-dependency and self-dependency, respectively. To this end, we design a multi-hypergraph neural network for ERMC. In particular, we combine average aggregation and attention aggregation to generate hyperedge features, which can allow utterance information to be better utilized. The experimental results show that our method outperforms multiple baselines, indicating that further exploitation of inter-dependency is of great value for ERMC. In addition, we also achieved good results on the emotional shift issue.