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This paper investigates the Relation Extraction task in documents bybenchmarking two different neural network models: a multi-modal language model(LayoutXLM) and a Graph Neural Network: Edge Convolution Network (ECN). Forthis benchmark, we use the XFUND dataset, released along with LayoutXLM. Whileboth models reach similar results, they both exhibit very differentcharacteristics. This raises the question on how to integrate variousmodalities in a neural network: by merging all modalities thanks to additionalpretraining (LayoutXLM), or in a cascaded way (ECN). We conclude by discussingsome methodological issues that must be considered for new datasets and taskdefinition in the domain of Information Extraction with complex documents.

LayoutXLM vs. GNN: An Empirical Evaluation of Relation Extraction for Documents