Hybrid Curriculum Learning for Emotion Recognition in Conversation

• URL: http://arxiv.org/abs/2112.11718v1
• Date: Wed, 22 Dec 2021 08:02:58 GMT
• Title: Hybrid Curriculum Learning for Emotion Recognition in Conversation
• Authors: Lin Yang, Yi Shen, Yue Mao, Longjun Cai
• Abstract summary: Our framework consists of two curricula: (1) conversation-level curriculum (CC); and (2) utterance-level curriculum (UC) With the proposed model-agnostic hybrid curriculum learning strategy, we observe significant performance boosts over a wide range of existing ERC models.
• Score: 10.912215835115063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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