Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation (2022)

Abstract: 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. Leveraging the Prototypical Network, the SPCL targets at solving the imbalanced classification problem through contrastive learning and does not require a large batch size. Meanwhile, we design a difficulty measure function based on the distance between classes and introduce curriculum learning to alleviate the impact of extreme samples. We achieve state-of-the-art results on three widely used benchmarks. Further, we conduct analytical experiments to demonstrate the effectiveness of our proposed SPCL and curriculum learning strategy.

## introduction with the development of online social networks, capturing emotions during conversations has gained increasing attention in both academia and industry @xcite @xcite @xcite . emotion recognition in conversation (erc) is critical in many scenarios, such as chatbots, healthcare applications, mining opinions on social media, and so on @xcite . the erc task aims to identify different emotions at each turn within a conversation based on the transcript. a conversation often contains several speakers and runs several turns; thus, emotions can vary drastically during the conversation. compared to traditional text classification tasks, figuring out ⋆ work done during internship at alibaba group. ## conclusion in this paper, we propose a novel loss function called supervised prototypical contrastive learning (spcl) loss for the emotion recognition in conversation task. combining with prototypical network, the spcl loss outperforms the traditional supervised contrastive learning loss. it also works well on class-imbalanced data and is less sensitive to the training batch size, which reduces the requirement of computing resource. to further exploit the power of contrastive learning on erc tasks, we design a distance-based difficulty measure function and introduce curriculum learning to alleviate the impact of extreme samples. we conduct experiments on three widely used benchmarks: iemo-cap, meld, and emorynlp. results show that our approach achieves state-of-the-art performance on all three datasets.