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Data augmentation techniques have been widely used to improve machinelearning performance as they enhance the generalization capability of models.In this work, to generate high quality synthetic data for low-resource taggingtasks, we propose a novel augmentation method with language models trained onthe linearized labeled sentences. Our method is applicable to both supervisedand semi-supervised settings. For the supervised settings, we conduct extensiveexperiments on named entity recognition (NER), part of speech (POS) tagging andend-to-end target based sentiment analysis (E2E-TBSA) tasks. For thesemi-supervised settings, we evaluate our method on the NER task under theconditions of given unlabeled data only and unlabeled data plus a knowledgebase. The results show that our method can consistently outperform thebaselines, particularly when the given gold training data are less.

DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks