@article{Yang_Shen_Mao_Cai_2022, title={Hybrid Curriculum Learning for Emotion Recognition in Conversation}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/21413}, DOI={10.1609/aaai.v36i10.21413}, abstractNote={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.}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yang, Lin and Shen, YI and Mao, Yue and Cai, Longjun}, year={2022}, month={Jun.}, pages={11595-11603} }