Improving Unsupervised Relation Extraction by Augmenting Diverse
  Sentence Pairs
Qing Wang Kang Zhou Qiao Qiao Yuepei Li Qi Li
Unsupervised relation extraction (URE) aims to extract relations between
named entities from raw text without requiring manual annotations or
pre-existing knowledge bases. In recent studies of URE, researchers put a
notable emphasis on contrastive learning strategies for acquiring relation
representations. However, these studies often overlook two important aspects:
the inclusion of diverse positive pairs for contrastive learning and the
exploration of appropriate loss functions. In this paper, we propose AugURE
with both within-sentence pairs augmentation and augmentation through
cross-sentence pairs extraction to increase the diversity of positive pairs and
strengthen the discriminative power of contrastive learning. We also identify
the limitation of noise-contrastive estimation (NCE) loss for relation
representation learning and propose to apply margin loss for sentence pairs.
Experiments on NYT-FB and TACRED datasets demonstrate that the proposed
relation representation learning and a simple K-Means clustering achieves
state-of-the-art performance.