MLGAT: Multi-Scale Line Graph Attention Network for Emotion Recognition in Conversation

Abstract

Emotion Recognition in Conversation (ERC) plays an important role in intelligent human-computer interaction. Highly accurate emotion recognition helps to improve the ability of machines to serve humans. Recent works exhibit poor generalization ability and low recognition accuracy, and better performances can only be presented on specific datasets. To be able to adapt to complex real-world application scenarios, we propose a novel model, termed Multi-scale Line Graph ATtention Network for Emotion Recognition in Conversation (MLGAT). MLGAT mines the emotional information dependent on the target utterance by focusing on local context and global context at different scales. Experiments show that our model achieves the second-highest performance among all current methods on both IEMOCAP and MELD datasets. Wa-F1 scores are 71.49% and 75.08%, respectively, which are only slightly different from their respective SOTAs (state of the art). Moreover, the model can show high accuracy in a few classes without additional measures.