Few-shot Relation Extraction (FSRE) aims to extract relational facts from a
sparse set of labeled corpora. Recent studies have shown promising results in
FSRE by employing Pre-trained Language Models (PLMs) within the framework of
supervised contrastive learning, which considers both instances and label
facts. However, how to effectively harness massive instance-label pairs to
encompass the learned representation with semantic richness in this learning
paradigm is not fully explored. To address this gap, we introduce a novel
synergistic anchored contrastive pre-training framework. This framework is
motivated by the insight that the diverse viewpoints conveyed through
instance-label pairs capture incomplete yet complementary intrinsic textual
semantics. Specifically, our framework involves a symmetrical contrastive
objective that encompasses both sentence-anchored and label-anchored
contrastive losses. By combining these two losses, the model establishes a
robust and uniform representation space. This space effectively captures the
reciprocal alignment of feature distributions among instances and relational
facts, simultaneously enhancing the maximization of mutual information across
diverse perspectives within the same relation. Experimental results demonstrate
that our framework achieves significant performance enhancements compared to
baseline models in downstream FSRE tasks. Furthermore, our approach exhibits
superior adaptability to handle the challenges of domain shift and zero-shot
relation extraction. Our code is available online at
this https URL

通过预训练语言模型和锚定对比预训练框架，本研究在少样本关系抽取中取得了显著的性能提升，并展示了对领域迁移和零样本关系抽取的优越适应能力。