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 in a supervised manner. SACL applies contrast-aware adversarial training to generate worst-case samples and uses joint class-spread contrastive learning to extract structured representations. 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 (CAT) strategy to learn more diverse features from context and enhance the model's context robustness. Under the framework with CAT, we develop a sequence-based SACL-LSTM to learn label-consistent and context-robust features for ERC. Experiments on three datasets show that SACL-LSTM achieves state-of-the-art performance on ERC. Extended experiments prove the effectiveness of SACL and CAT.

翻译：提取泛化且鲁棒的特征是对话情感识别（ERC）中的主要挑战。为此，我们提出了一种监督式对抗对比学习（SACL）框架，以监督方式学习类扩展的结构化表示。SACL采用对比感知的对抗训练生成最坏情况样本，并利用联合类扩展对比学习提取结构化表示，能够有效利用标签级别的特征一致性并保留细粒度的类内特征。为避免对抗扰动对上下文依赖性数据的负面影响，我们设计了上下文对抗训练（CAT）策略，从上下文中学习更多样化的特征并增强模型的上下文鲁棒性。在CAT框架下，我们开发了基于序列的SACL-LSTM模型，用于学习标签一致且上下文鲁棒的ERC特征。在三个数据集上的实验表明，SACL-LSTM在ERC任务中达到了最先进性能。扩展实验证明了SACL与CAT的有效性。