  Document Understanding is an evolving field in Natural Language Processing
(NLP). In particular, visual and spatial features are essential in addition to
the raw text itself and hence, several multimodal models were developed in the
field of Visual Document Understanding (VDU). However, while research is mainly
focused on Key Information Extraction (KIE), Relation Extraction (RE) between
identified entities is still under-studied. For instance, RE is crucial to
regroup entities or obtain a comprehensive hierarchy of data in a document. In
this paper, we present a model that, initialized from LayoutLMv3, can match or
outperform the current state-of-the-art results in RE applied to Visually-Rich
Documents (VRD) on FUNSD and CORD datasets, without any specific pre-training
and with fewer parameters. We also report an extensive ablation study performed
on FUNSD, highlighting the great impact of certain features and modelization
choices on the performances.
