FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues

Yingmei Guo1, Zhiyong Wu1, Mingxing Xu2
1Tsinghua University, 2Key Laboratory of Pervasive Computing, Ministry of Education Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China


Unlike non-conversation scenes, emotion recognition in dialogues (ERD) poses more complicated challenges due to its interactive nature and intricate contextual information. All present methods model historical utterances without considering the content of the target utterance. However, different parts of a historical utterance may contribute differently to emotion inference of different target utterances. Therefore we propose Fine-grained Extraction and Reasoning Network (FERNet) to generate target-specific historical utterance representations. The reasoning module effectively handles both local and global sequential dependencies to reason over context, and updates target utterance representations to more informed vectors. Experiments on two benchmarks show that our method achieves competitive performance compared with previous methods.