Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation

Understand

Capturing emotions within a conversation plays an essential role in modern dialogue systems.

• However, the weak correlation between emotions and semantics brings many challenges to emotion recognition in conversation (ERC).
• Even semantically similar utterances, the emotion may vary drastically depending on contexts or speakers.
• In this paper, we propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task.