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Visual Relation Extraction (VRE) is a powerful means of discoveringrelationships between entities within visually-rich documents. Existing methodsoften focus on manipulating entity features to find pairwise relations, yetneglect the more fundamental structural information that links disparate entitypairs together. The absence of global structure information may make the modelstruggle to learn long-range relations and easily predict conflicted results.To alleviate such limitations, we propose a GlObal Structure knowledge-guidedrelation Extraction (GOSE) framework. GOSE initiates by generating preliminaryrelation predictions on entity pairs extracted from a scanned image of thedocument. Subsequently, global structural knowledge is captured from thepreceding iterative predictions, which are then incorporated into therepresentations of the entities. This "generate-capture-incorporate" cycle isrepeated multiple times, allowing entity representations and global structureknowledge to be mutually reinforced. Extensive experiments validate that GOSEnot only outperforms existing methods in the standard fine-tuning setting butalso reveals superior cross-lingual learning capabilities; indeed, even yieldsstronger data-efficient performance in the low-resource setting. The code forGOSE will be available at https://github.com/chenxn2020/GOSE.

Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document