Text augmentation is one of the most effective techniques to solve the
critical problem of insufficient data in text classification. Existing text
augmentation methods achieve hopeful performance in few-shot text data
augmentation. However, these methods usually lead to performance degeneration
on public datasets due to poor quality augmentation instances. Our study shows
that even employing pre-trained language models, existing text augmentation
methods generate numerous low-quality instances and lead to the feature space
shift problem in augmentation instances. However, we note that the pre-trained
language model is good at finding low-quality instances provided that it has
been fine-tuned on the target dataset. To alleviate the feature space shift and
performance degeneration in existing text augmentation methods, we propose
BOOSTAUG, which reconsiders the role of the language model in text augmentation
and emphasizes the augmentation instance filtering rather than generation. We
evaluate BOOSTAUG on both sentence-level text classification and aspect-based
sentiment classification. The experimental results on seven commonly used text
classification datasets show that our augmentation method obtains
state-of-the-art performance. Moreover, BOOSTAUG is a flexible framework; we
release the code which can help improve existing augmentation methods.

本研究提出 BOOSTAUG，这个基于预训练语言模型的文本增强方法重点在于增强实例过滤，而不是生成，解决现有文本增强方法中的性能下降和特征空间漂移等问题。结果表明，在句子级文本分类和基于方面的情感分类上，BOOSTAUG 均取得了最先进的性能，该方法是灵活的，可以改进现有的增强方法。