Improving few-shot relation extraction through semantics-guided learning

Abstract

Few-shot relation extraction (few-shot RE) aims to recognize relations between the entity pair in a given text by utilizing very few annotated instances. As a simple yet efficient approach, prototype network-based methods often directly incorporate relation information to enhance prototype representation or leverage contrastive learning to mitigate prediction confusion. Despite achieving good results, the above methods are still susceptible to false judgments of outlier samples and confusion of similar classes. To address these issues, we propose a novel Semantics-Guided Learning (SemGL) method that more effectively utilizes relation information to enhance both the representations of instances and prototypes for improving the performance of few-shot RE. First, SemGL employs the prompt encoder to encode various prompt templates of instances and relation information and obtains more accurate semantic representations of instances, instance prototypes, and concept prototypes via the prompt enhancement from large language models. Then, SemGL introduces a novel technique called relation graph learning, which leverages concept prototypes to cluster homogeneous instances together, emphasizing relation-specific features of concrete instances. Simultaneously, SemGL employs instance-level contrastive learning between instance prototypes and support instances to distinguish between intra-class instances and inter-class instances to promote shared features among intra-class instances. Additionally, prototype-level contrastive learning leverages concept prototypes to pull closer relation-specific features of the concept prototype and shared features of the instance prototype from the same relation. Finally, SemGL utilizes new relation prototypes that integrate interpretable features of concept prototypes and shared features of instance prototypes for prediction. Experimental results on two publicly available few-shot RE datasets demonstrate the effectiveness and efficiency of SemGL in introducing relation information, with particularly promising results for the domain adaptation challenge task.