Consistent Representation Learning for Continual Relation Extraction

Understand

Continual relation extraction (CRE) aims to continuously train a model on data with new relations while avoiding forgetting old ones.

• Some previous work has proved that storing a few typical samples of old relations and replaying them when learning new relations can effectively avoid forgetting.
• However, these memory-based methods tend to overfit the memory samples and perform poorly on imbalanced datasets.
• To solve these challenges, a consistent representation learning method is proposed, which maintains the stability of the relation embedding by adopting contrastive learning and knowledge distillation when replaying memory.