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.Comment: 16 pages, accepted by ACL 202