Hybrid Curriculum Learning for Emotion Recognition in Conversation
Keywords:Speech & Natural Language Processing (SNLP)
AbstractEmotion 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.
How to Cite
Yang, L., Shen, Y., Mao, Y., & Cai, L. (2022). Hybrid Curriculum Learning for Emotion Recognition in Conversation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 11595-11603. https://doi.org/10.1609/aaai.v36i10.21413
AAAI Technical Track on Speech and Natural Language Processing