  Biomedical Named Entity Recognition (BioNER) is the fundamental task of
identifying named entities from biomedical text. However, BioNER suffers from
severe data scarcity and lacks high-quality labeled data due to the highly
specialized and expert knowledge required for annotation. Though data
augmentation has shown to be highly effective for low-resource NER in general,
existing data augmentation techniques fail to produce factual and diverse
augmentations for BioNER. In this paper, we present BioAug, a novel data
augmentation framework for low-resource BioNER. BioAug, built on BART, is
trained to solve a novel text reconstruction task based on selective masking
and knowledge augmentation. Post training, we perform conditional generation
and generate diverse augmentations conditioning BioAug on selectively corrupted
text similar to the training stage. We demonstrate the effectiveness of BioAug
on 5 benchmark BioNER datasets and show that BioAug outperforms all our
baselines by a significant margin (1.5%-21.5% absolute improvement) and is able
to generate augmentations that are both more factual and diverse. Code:
https://github.com/Sreyan88/BioAug.
