LayoutXLM vs. GNN: An Empirical Evaluation of Relation Extraction for Documents
This work addresses relation extraction for complex documents, providing an empirical comparison that is incremental in nature.
The paper benchmarks LayoutXLM and a Graph Neural Network (ECN) for relation extraction in documents using the XFUND dataset, finding they achieve similar results but differ in how they integrate modalities.
This paper investigates the Relation Extraction task in documents by benchmarking two different neural network models: a multi-modal language model (LayoutXLM) and a Graph Neural Network: Edge Convolution Network (ECN). For this benchmark, we use the XFUND dataset, released along with LayoutXLM. While both models reach similar results, they both exhibit very different characteristics. This raises the question on how to integrate various modalities in a neural network: by merging all modalities thanks to additional pretraining (LayoutXLM), or in a cascaded way (ECN). We conclude by discussing some methodological issues that must be considered for new datasets and task definition in the domain of Information Extraction with complex documents.