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Forms are a common type of document in real life and carry rich informationthrough textual contents and the organizational structure. To realize automaticprocessing of forms, word grouping and relation extraction are two fundamentaland crucial steps after preliminary processing of optical character reader(OCR). Word grouping is to aggregate words that belong to the same semanticentity, and relation extraction is to predict the links between semanticentities. Existing works treat them as two individual tasks, but these twotasks are correlated and can reinforce each other. The grouping process willrefine the integrated representation of the corresponding entity, and thelinking process will give feedback to the grouping performance. For thispurpose, we acquire multimodal features from both textual data and layoutinformation and build an end-to-end model through multitask training to combineword grouping and relation extraction to enhance performance on each task. Wevalidate our proposed method on a real-world, fully-annotated, noisy-scannedbenchmark, FUNSD, and extensive experiments demonstrate the effectiveness ofour method.

GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding