End-to-End Hierarchical Relation Extraction for Generic Form
  Understanding
  Form understanding is a challenging problem which aims to recognize semantic
entities from the input document and their hierarchical relations. Previous
approaches face significant difficulty dealing with the complexity of the task,
thus treat these objectives separately. To this end, we present a novel deep
neural network to jointly perform both entity detection and link prediction in
an end-to-end fashion. Our model extends the Multi-stage Attentional U-Net
architecture with the Part-Intensity Fields and Part-Association Fields for
link prediction, enriching the spatial information flow with the additional
supervision from entity linking. We demonstrate the effectiveness of the model
on the Form Understanding in Noisy Scanned Documents (FUNSD) dataset, where our
method substantially outperforms the original model and state-of-the-art
baselines in both Entity Labeling and Entity Linking task.
