  Many business workflows require extracting important fields from form-like
documents (e.g. bank statements, bills of lading, purchase orders, etc.).
Recent techniques for automating this task work well only when trained with
large datasets. In this work we propose a novel data augmentation technique to
improve performance when training data is scarce, e.g. 10-250 documents. Our
technique, which we call FieldSwap, works by swapping out the key phrases of a
source field with the key phrases of a target field to generate new synthetic
examples of the target field for use in training. We demonstrate that this
approach can yield 1-7 F1 point improvements in extraction performance.
