SDTN: Speaker Dynamics Tracking Network for Emotion Recognition in Conversation

Jiawei Chen, Peijie Huang, Guotai Huang, Qianer Li, Yuhong Xu

Abstract: Emotion Recognition in Conversation (ERC) has considerable prospects due to its wide range of applications. Most existing works integrate speaker information statically and capture a relatively consistent atmosphere in conversation. However, these works poorly track the emotional state dynamics of each party in a conversation and focus on emotion consistency. The speakers’ emotional states are independent but influence each other during the conversation. To address the above issues, we propose a Speaker Dynamics Tracking Network (SDTN) for ERC. Specifically, SDTN can dynamically track the local and global speaker states during emotional flow in conversation and capture implicit stimulation of emotional shift. Extensive experiments on MELD and EmoryNLP datasets demonstrate the superiority and effectiveness of our proposed SDTN model, and confirm that every designed module consistently benefits the performance.