论文标题
HIURE：无监督关系提取的分层示例性学习
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
论文作者
论文摘要
无监督的关系提取旨在从自然语言句子中提取实体之间的关系，而无需事先有关关系范围或分发的信息。现有作品要么利用自我监管的方案来通过迭代地利用自适应聚类和分类来完善关系特征信号，从而引起逐渐漂移问题，或者采用实例对比度学习，这使得不合理地将这些句子对分开，而这些句子在语义上相似。为了克服这些缺陷，我们提出了一个名为HIURE的新型对比学习框架，该框架具有使用交叉层次结构注意从关系特征空间中得出层次信号的能力，并有效地优化了在示例性的对比度学习下的句子的关系表示。两个公共数据集的实验结果表明，与最先进的模型相比，HIURE对无监督关系提取的高级有效性和鲁棒性。
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.
