  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.
