  Current work in named entity recognition (NER) shows that data augmentation
techniques can produce more robust models. However, most existing techniques
focus on augmenting in-domain data in low-resource scenarios where annotated
data is quite limited. In contrast, we study cross-domain data augmentation for
the NER task. We investigate the possibility of leveraging data from
high-resource domains by projecting it into the low-resource domains.
Specifically, we propose a novel neural architecture to transform the data
representation from a high-resource to a low-resource domain by learning the
patterns (e.g. style, noise, abbreviations, etc.) in the text that
differentiate them and a shared feature space where both domains are aligned.
We experiment with diverse datasets and show that transforming the data to the
low-resource domain representation achieves significant improvements over only
using data from high-resource domains.
