  Text augmentation is an effective technique for addressing the problem of
insufficient data in natural language processing. However, existing text
augmentation methods tend to focus on few-shot scenarios and usually perform
poorly on large public datasets. Our research indicates that existing
augmentation methods often generate instances with shifted feature spaces,
which leads to a drop in performance on the augmented data (for example, EDA
generally loses $\approx 2\%$ in aspect-based sentiment classification). To
address this problem, we propose a hybrid instance-filtering framework
(BootAug) based on pre-trained language models that can maintain a similar
feature space with natural datasets. BootAug is transferable to existing text
augmentation methods (such as synonym substitution and back translation) and
significantly improves the augmentation performance by $\approx 2-3\%$ in
classification accuracy. Our experimental results on three classification tasks
and nine public datasets show that BootAug addresses the performance drop
problem and outperforms state-of-the-art text augmentation methods.
Additionally, we release the code to help improve existing augmentation methods
on large datasets.
