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.

翻译：对话情感识别（ERC）旨在检测对话中每条话语背后的情感类别。如何有效生成话语表示仍是该任务的关键挑战。近期研究提出了多种模型试图解决该问题，但在区分兴奋与快乐等相似情感时仍存在困难。为缓解这一问题，我们提出了一种锚定情感对比学习（EACL）框架，能够为相似情感生成更具区分性的话语表示。具体而言，我们利用标签编码作为锚点引导话语表示的学习，并设计辅助损失函数确保相似情感对应的锚点有效分离。此外，我们还引入额外自适应过程，使锚点可作为有效分类器以提升分类性能。大量实验表明，所提出的EACL方法在情感识别上达到了最先进水平，并在相似情感识别中展现出优越性能。我们的代码已开源至https://github.com/Yu-Fangxu/EACL。