Few-shot relation classification (FSRC) 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 FSRC tasks with similar categories that confuse the model to distinguish correctly. We argue this is largely due to two reasons, 1) ignoring pivotal and discriminate information that is crucial to distinguish confusing classes, and 2) training indiscriminately via randomly sampled tasks of varying difficulty. In this article, we introduce a novel prototypical network approach with contrastive learning that learns more informative and discriminative representations by exploiting relation label information. We further design two strategies that increase the difficulty of training tasks and allow the model to adaptively learn to focus on hard tasks. By doing so, our model can better represent subtle inter-relation variance and grow up through task difficulty. Extensive experiments on three standard benchmarks demonstrate the effectiveness of our method.

Few-shot relation extraction (FSRE) aims to predict the relation for a pair of entities in a sentence by exploring a few labeled instances for each relation type. Current methods mainly rely on meta-learning to learn generalized representations by optimizing the network parameters based on various collections of tasks sampled from training data. However, these methods may suffer from two main issues. 1) Insufficient supervision of meta-learning to learn discriminative representations on very few training instances, which are sampled from a large amount of base class data. 2) Spurious correlations between entities and relation types due to the biased training procedure that focuses more on entity pair rather than context. To learn more discriminative and unbiased representations for FSRE, this paper proposes a two-stage approach via supervised contrastive learning and sentence- and entity-level prototypical networks. In the first (pre-training) stage, we introduce a supervised contrastive pre-training method, which is able to yield more discriminative representations by learning from the entire training instances, such that the semantically related representations are close to each other, and far away otherwise. In the second (meta-learning) stage, we propose a novel sentence- and entity-level prototypical network equipped with fine-grained feature-wise fusion strategy to learn unbiased representations, where the networks are initialized with the parameters trained in the first stage. Specifically, the proposed network consists of a sentence branch and an entity branch, taking entire sentences and entity mentions as inputs, respectively. The entity branch explicitly captures the correlation between entity pairs and relations, and then dynamically adjusts the sentence branch's prediction distributions. By doing so, the spurious correlations issue caused by biased training samples can be properly mitigated. Extensive experiments on two FSRE benchmarks demonstrate the effectiveness of our approach.