Relational triple extraction is crucial work for the automatic construction of knowledge graphs. Existing methods only construct shallow representations from a token or token pair-level. However, previous works ignore local spatial dependencies of relational triples, resulting in a weakness of entity pair boundary detection. To tackle this problem, we propose a novel Region-based Table Filling method (RTF). We devise a novel region-based tagging scheme and bi-directional decoding strategy, which regard each relational triple as a region on the relation-specific table, and identifies triples by determining two endpoints of each region. We also introduce convolution to construct region-level table representations from a spatial perspective which makes triples easier to be captured. In addition, we share partial tagging scores among different relations to improve learning efficiency of relation classifier. Experimental results show that our method achieves state-of-the-art with better generalization capability on three variants of two widely used benchmark datasets.

翻译：关系三元组抽取是知识图谱自动构建中的关键任务。现有方法仅从词元或词元对层面构建浅层表示，但忽略了关系三元组的局部空间依赖关系，导致实体对边界检测能力不足。为解决该问题，我们提出了一种新颖的基于区域的表格填充方法（Region-based Table Filling, RTF）。我们设计了创新的基于区域的标注方案与双向解码策略，将每个关系三元组视为关系特定表格上的一个区域，并通过确定每个区域的端点来识别三元组。同时，引入卷积操作从空间角度构建区域级表格表示，使三元组更易于捕获。此外，我们在不同关系间共享部分标注分数，以提升关系分类器的学习效率。实验结果表明，在两个广泛使用的基准数据集的三类变体上，本方法达到了最优性能，并展现出更强的泛化能力。