Emotion-Anchored Contrastive Learning Framework for Emotion Recognition
  in Conversation
Fangxu Yu Junjie Guo Zhen Wu Xinyu Dai
Emotion Recognition in Conversation (ERC) involves detecting the underlying
emotion behind each utterance within a conversation. Effectively generating
representations for utterances remains a significant challenge in this task.
Recent works propose various models to address this issue, but they still
struggle with differentiating similar emotions such as excitement and
happiness. To alleviate this problem, We propose an Emotion-Anchored
Contrastive Learning (EACL) framework that can generate more distinguishable
utterance representations for similar emotions. To achieve this, we utilize
label encodings as anchors to guide the learning of utterance representations
and design an auxiliary loss to ensure the effective separation of anchors for
similar emotions. Moreover, an additional adaptation process is proposed to
adapt anchors to serve as effective classifiers to improve classification
performance. Across extensive experiments, our proposed EACL achieves
state-of-the-art emotion recognition performance and exhibits superior
performance on similar emotions. Our code is available at
https://github.com/Yu-Fangxu/EACL.