  Few-shot relation extraction (FSRE) focuses on recognizing novel relations by
learning with merely a handful of annotated instances. Meta-learning has been
widely adopted for such a task, which trains on randomly generated few-shot
tasks to learn generic data representations. Despite impressive results
achieved, existing models still perform suboptimally when handling hard FSRE
tasks, where the relations are fine-grained and similar to each other. We argue
this is largely because existing models do not distinguish hard tasks from easy
ones in the learning process. In this paper, we introduce a novel approach
based on contrastive learning that learns better representations by exploiting
relation label information. We further design a method that allows the model to
adaptively learn how to focus on hard tasks. Experiments on two standard
datasets demonstrate the effectiveness of our method.
