  Visual Relation Extraction (VRE) is a powerful means of discovering
relationships between entities within visually-rich documents. Existing methods
often focus on manipulating entity features to find pairwise relations, yet
neglect the more fundamental structural information that links disparate entity
pairs together. The absence of global structure information may make the model
struggle to learn long-range relations and easily predict conflicted results.
To alleviate such limitations, we propose a GlObal Structure knowledge-guided
relation Extraction (GOSE) framework. GOSE initiates by generating preliminary
relation predictions on entity pairs extracted from a scanned image of the
document. Subsequently, global structural knowledge is captured from the
preceding iterative predictions, which are then incorporated into the
representations of the entities. This "generate-capture-incorporate" cycle is
repeated multiple times, allowing entity representations and global structure
knowledge to be mutually reinforced. Extensive experiments validate that GOSE
not only outperforms existing methods in the standard fine-tuning setting but
also reveals superior cross-lingual learning capabilities; indeed, even yields
stronger data-efficient performance in the low-resource setting. The code for
GOSE will be available at https://github.com/chenxn2020/GOSE.
