  Automating information extraction from form-like documents at scale is a
pressing need due to its potential impact on automating business workflows
across many industries like financial services, insurance, and healthcare. The
key challenge is that form-like documents in these business workflows can be
laid out in virtually infinitely many ways; hence, a good solution to this
problem should generalize to documents with unseen layouts and languages. A
solution to this problem requires a holistic understanding of both the textual
segments and the visual cues within a document, which is non-trivial. While the
natural language processing and computer vision communities are starting to
tackle this problem, there has not been much focus on (1) data-efficiency, and
(2) ability to generalize across different document types and languages.
  In this paper, we show that when we have only a small number of labeled
documents for training (~50), a straightforward transfer learning approach from
a considerably structurally-different larger labeled corpus yields up to a 27
F1 point improvement over simply training on the small corpus in the target
domain. We improve on this with a simple multi-domain transfer learning
approach, that is currently in production use, and show that this yields up to
a further 8 F1 point improvement. We make the case that data efficiency is
critical to enable information extraction systems to scale to handle hundreds
of different document-types, and learning good representations is critical to
accomplishing this.
