Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations. The framework applies contrast-aware adversarial training to generate worst-case samples and uses a joint class-spread contrastive learning objective on both original and adversarial samples. It can effectively utilize label-level feature consistency and retain fine-grained intra-class features. To avoid the negative impact of adversarial perturbations on context-dependent data, we design a contextual adversarial training strategy to learn more diverse features from context and enhance the model's context robustness. We develop a sequence-based method SACL-LSTM under this framework, to learn label-consistent and context-robust emotional features for ERC. Experiments on three datasets demonstrate that SACL-LSTM achieves state-of-the-art performance on ERC. Extended experiments prove the effectiveness of the SACL framework.

本文提出了一种监督对抗性对比学习策略，包括对抗样本生成和类层间的对比学习目标，并通过序列方法 SACL-LSTM 实现了 ERC 中标签一致和上下文鲁棒的情感特征学习，具有很强的泛化和鲁棒性。

对话情感识别的监督对抗性对比学习