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In this work, we take the named entity recognition task in the Englishlanguage as a case study and explore style transfer as a data augmentationmethod to increase the size and diversity of training data in low-resourcescenarios. We propose a new method to effectively transform the text from ahigh-resource domain to a low-resource domain by changing its style-relatedattributes to generate synthetic data for training. Moreover, we design aconstrained decoding algorithm along with a set of key ingredients for dataselection to guarantee the generation of valid and coherent data. Experimentsand analysis on five different domain pairs under different data regimesdemonstrate that our approach can significantly improve results compared tocurrent state-of-the-art data augmentation methods. Our approach is a practicalsolution to data scarcity, and we expect it to be applicable to other NLPtasks.

Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition