Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning. Experimental results on two public datasets demonstrate the advanced effectiveness and robustness of HiURE on unsupervised relation extraction when compared with state-of-the-art models.

翻译：无监督关系抽取旨在从自然语言句子中提取实体间的关系，且无需预先了解关系的范围或分布。现有方法要么利用自监督方案通过迭代自适应聚类与分类来优化关系特征信号，从而引发渐进漂移问题；要么采用实例级对比学习，不合理地将语义相似的句子对相互推离。为克服这些缺陷，我们提出了一种名为HiURE的新型对比学习框架，该框架能通过跨层级注意力从关系特征空间中提取分层信号，并基于范例级对比学习有效优化句子关系表示。在两个公开数据集上的实验结果表明，与最先进模型相比，HiURE在无监督关系抽取任务上展现出卓越的有效性和鲁棒性。