Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2306.01505v2
• Date: Sun, 9 Jul 2023 23:27:27 GMT
• Title: Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
• Authors: Dou Hu, Yinan Bao, Lingwei Wei, Wei Zhou, Songlin Hu
• Abstract summary: We propose a framework for learning class-spread structured representations in a supervised manner. It can effectively utilize label-level feature consistency and retain fine-grained intra-class features. Under the framework with CAT, we develop a sequence-based SACL-LSTM to learn label-consistent and context-robust features.
• Score: 24.542445315345464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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