Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors

arXiv:2402.15713v1 Announce Type: new
Abstract: 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 …

abstract arxiv capabilities catastrophic forgetting challenges continual cs.ai cs.cl data explore extraction few-shot language language models learn making novel overfitting paper practical prompt prompt learning relations training training data type