Title: Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs Show Chinese title

Title: 通过增强多样句对改进无监督关系抽取

Abstract: 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. Show Chinese abstract

Abstract: 无监督关系抽取（URE）旨在从原始文本中提取命名实体之间的关系，而无需手动标注或预先存在的知识库。 在URE的近期研究中，研究人员特别强调了对比学习策略以获取关系表示。 然而，这些研究常常忽略了两个重要方面：对比学习中多样正对的包含以及适当损失函数的探索。 在本文中，我们提出了AugURE，通过句子内对增强和跨句子对提取来增加正对的多样性，并增强对比学习的区分能力。 我们还指出了噪声对比估计（NCE）损失在关系表示学习中的局限性，并提出对句子对应用边界损失。 在NYT-FB和TACRED数据集上的实验表明，所提出的的关系表示学习和简单的K-Means聚类达到了最先进的性能。