A Span Extraction Approach for Information Extraction on Visually-Rich
  Documents
  Information extraction (IE) for visually-rich documents (VRDs) has achieved
SOTA performance recently thanks to the adaptation of Transformer-based
language models, which shows the great potential of pre-training methods. In
this paper, we present a new approach to improve the capability of language
model pre-training on VRDs. Firstly, we introduce a new query-based IE model
that employs span extraction instead of using the common sequence labeling
approach. Secondly, to further extend the span extraction formulation, we
propose a new training task that focuses on modelling the relationships among
semantic entities within a document. This task enables target spans to be
extracted recursively and can be used to pre-train the model or as an IE
downstream task. Evaluation on three datasets of popular business documents
(invoices, receipts) shows that our proposed method achieves significant
improvements compared to existing models. The method also provides a mechanism
for knowledge accumulation from multiple downstream IE tasks.
