Few-shot relation extraction aims to recognize novel relations with few labeled sentences in each relation. Previous metric-based few-shot relation extraction algorithms identify relationships by comparing the prototypes generated by the few labeled sentences embedding with the embeddings of the query sentences using a trained metric function. However, as these domains always have considerable differences from those in the training dataset, the generalization ability of these approaches on unseen relations in many domains is limited. Since the prototype is necessary for obtaining relationships between entities in the latent space, we suggest learning more interpretable and efficient prototypes from prior knowledge and the intrinsic semantics of relations to extract new relations in various domains more effectively. By exploring the relationships between relations using prior information, we effectively improve the prototype representation of relations. By using contrastive learning to make the classification margins between sentence embedding more distinct, the prototype's geometric interpretability is enhanced. Additionally, utilizing a transfer learning approach for the cross-domain problem allows the generation process of the prototype to account for the gap between other domains, making the prototype more robust and enabling the better extraction of associations across multiple domains. The experiment results on the benchmark FewRel dataset demonstrate the advantages of the suggested method over some state-of-the-art approaches.

翻译：小样本关系抽取旨在通过每个关系中少量的带标签句子识别新型关系。以往的基于度量的小样本关系抽取算法通过比较由少量带标签句子嵌入生成的原型与查询句子的嵌入，利用训练好的度量函数来识别关系。然而，由于这些领域往往与训练数据集存在显著差异，这些方法在众多领域中对未见过关系的泛化能力受限。鉴于原型对于获取潜在空间中实体间关系至关重要，我们建议从先验知识和关系内在语义中学习更具解释性和高效性的原型，以更有效地提取不同领域中的新关系。通过利用先验信息探索关系之间的关联，我们有效改进了关系的原型表示。采用对比学习使句子嵌入间的分类边界更加明晰，从而增强了原型的几何可解释性。此外，针对跨域问题运用迁移学习方法，使原型生成过程能够考虑不同领域间的差异，从而提升原型的鲁棒性，并更好地实现跨多领域关联的抽取。在基准FewRel数据集上的实验结果表明，所提方法相较于一些现有最优方法具有优势。