On-the-fly Denoising for Data Augmentation in Natural Language Understanding

Data Augmentation (DA) is frequently used to provide additional training data without extra human annotation automatically. However, data augmentation may introduce noisy data that impairs training. To guarantee the quality of augmented data, existing methods either assume no noise exists in the augmented data and adopt consistency training or use simple heuristics such as training loss and diversity constraints to filter out "noisy" data. However, those filtered examples may still contain useful information, and dropping them completely causes a loss …

annotation augmentation augmented data cs.ai cs.cl data denoising extra fly human language language understanding natural natural language noise quality simple training training data understanding