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

Understand

Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models.

• In this work, to generate high quality synthetic data for low-resource tagging tasks, we propose a novel augmentation method with language models trained on the linearized labeled sentences.
• Our method is applicable to both supervised and semi-supervised settings.
• For the supervised settings, we conduct extensive experiments on named entity recognition (NER), part of speech (POS) tagging and end-to-end target based sentiment analysis (E2E-TBSA) tasks.