Hybrid Curriculum Learning for Emotion Recognition in Conversation

TL;DR

This paper introduces a hybrid curriculum learning framework for emotion recognition in conversation, improving model performance by ordering training data based on conversation difficulty and emotion similarity.

Contribution

It proposes a novel hybrid curriculum learning approach with conversation-level and utterance-level curricula for ERC, achieving state-of-the-art results.

Findings

Significant performance improvements over existing ERC models.

Achieved new state-of-the-art results on four datasets.

Effective curriculum strategies enhance emotion recognition accuracy.

Abstract

Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance. Motivated by recent studies which have proven that feeding training examples in a meaningful order rather than considering them randomly can boost the performance of models, we propose an ERC-oriented hybrid curriculum learning framework. Our framework consists of two curricula: (1) conversation-level curriculum (CC); and (2) utterance-level curriculum (UC). In CC, we construct a difficulty measurer based on "emotion shift" frequency within a conversation, then the conversations are scheduled in an "easy to hard" schema according to the difficulty score returned by the difficulty measurer. For UC, it is implemented from an emotion-similarity perspective, which progressively strengthens the model's ability in identifying the confusing emotions. With the proposed model-agnostic hybrid curriculum learning strategy, we observe significant performance boosts over a wide range of existing ERC models and we are able to achieve new state-of-the-art results on four public ERC datasets.