HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
This work addresses unsupervised relation extraction for natural language processing, offering a novel approach to improve accuracy without labeled data, though it appears incremental as it builds on contrastive learning methods.
The paper tackles the problem of unsupervised relation extraction by proposing HiURE, a hierarchical exemplar contrastive learning framework that overcomes issues like gradual drift and semantic similarity mismatches in existing methods, achieving state-of-the-art results on two public datasets.
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.