A Span Extraction Approach for Information Extraction on Visually-Rich Documents

Understand

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.