  The state of art natural language processing systems relies on sizable
training datasets to achieve high performance. Lack of such datasets in the
specialized low resource domains lead to suboptimal performance. In this work,
we adapt backtranslation to generate high quality and linguistically diverse
synthetic data for low-resource named entity recognition. We perform
experiments on two datasets from the materials science (MaSciP) and biomedical
domains (S800). The empirical results demonstrate the effectiveness of our
proposed augmentation strategy, particularly in the low-resource scenario.
