DIEU: A Dynamic Interaction Emotion Unit for Emotion Recognition in Conversation
Shu Guo Zhao, Weifeng Liu, Jie Chen, Xiao Na Sun · ACM Transactions on Asian and Low-Resource Language Information Processing · 2023
Emotion recognition in conversation (ERC) is challenging because the conversation takes place in real time and the speakers interact with each other. However, existing methods ignore the dynamic characteristics of interaction between speakers, and the problem of long-range context propagation still exists. In this article, we propose a dynamic interaction emotion unit to solve the preceding problems on the transcription of the conversation. First, we propose a main influence interval search algorithm to provide a dynamic interaction interval for each utterance. Then, we utilize the speaker-aware influence module and the two-stream context module to capture the dynamic interaction and the contextual information from this interval. Furthermore, to obtain the speaker state representation rich in emotional information, we propose a novel dynamic routing algorithm to fuse the preceding information. These well-integrated state representations also enable our model to capture contextual information at a longer distance. Experiments on multiple datasets demonstrate the effectiveness of the proposed method.