Making Pre-trained Language Models Better Continual Few-Shot Relation
  Extractors
Shengkun Ma Jiale Han Yi Liang Bo Cheng
Continual Few-shot Relation Extraction (CFRE) is a practical problem that
requires the model to continuously learn novel relations while avoiding
forgetting old ones with few labeled training data. The primary challenges are
catastrophic forgetting and overfitting. This paper harnesses prompt learning
to explore the implicit capabilities of pre-trained language models to address
the above two challenges, thereby making language models better continual
few-shot relation extractors. Specifically, we propose a Contrastive Prompt
Learning framework, which designs prompt representation to acquire more
generalized knowledge that can be easily adapted to old and new categories, and
margin-based contrastive learning to focus more on hard samples, therefore
alleviating catastrophic forgetting and overfitting issues. To further remedy
overfitting in low-resource scenarios, we introduce an effective memory
augmentation strategy that employs well-crafted prompts to guide ChatGPT in
generating diverse samples. Extensive experiments demonstrate that our method
outperforms state-of-the-art methods by a large margin and significantly
mitigates catastrophic forgetting and overfitting in low-resource scenarios.