Current research in form understanding predominantly relies on large
pre-trained language models, necessitating extensive data for pre-training.
However, the importance of layout structure (i.e., the spatial relationship
between the entity blocks in the visually rich document) to relation extraction
has been overlooked. In this paper, we propose REgion-Aware Relation Extraction
(RE$^2$) that leverages region-level spatial structure among the entity blocks
to improve their relation prediction. We design an edge-aware graph attention
network to learn the interaction between entities while considering their
spatial relationship defined by their region-level representations. We also
introduce a constraint objective to regularize the model towards consistency
with the inherent constraints of the relation extraction task. Extensive
experiments across various datasets, languages and domains demonstrate the
superiority of our proposed approach.

本文提出了一种基于区域感知的关系抽取方法 (RE$^2$)，它利用边缘感知的图注意力网络学习实体之间的交互，并考虑实体之间由其区域级表示定义的空间关系，同时引入约束目标以使模型符合关系抽取任务的内在约束。实验结果表明，我们的方法在各种数据集、语言和领域中具有优越性。