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 https://github.com/AONE-NLP/FSRE-SaCon.

翻译：少样本关系抽取（Few-shot Relation Extraction, FSRE）旨在从稀疏标注的语料中提取关系事实。近期研究表明，在监督对比学习框架下利用预训练语言模型（Pre-trained Language Models, PLMs）联合考虑实例与标签事实可取得显著成果。然而，如何在该学习范式中有效利用海量实例-标签对，赋予学得表示丰富的语义性，尚未得到充分探索。为弥补这一空白，我们提出了一种新颖的协同锚定对比预训练框架。该框架基于以下洞见：实例-标签对所传递的多元视角虽不完整，却能捕获互补的内在文本语义。具体而言，框架包含对称化对比目标函数，同时涵盖句子锚定与标签锚定的对比损失。通过结合这两种损失，模型构建了鲁棒且统一的表示空间，该空间有效捕获实例与关系事实间特征分布的双向对齐，同时增强同一关系下不同视角间的互信息最大化。实验结果表明，在各类下游FSRE任务中，我们的框架相较基线模型实现了显著的性能提升。此外，我们的方法在域转移和零样本关系抽取挑战中展现出更优的适应能力。相关代码已开源至https://github.com/AONE-NLP/FSRE-SaCon。