BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework

TL;DR

BootAug is a hybrid filtering framework that enhances text augmentation by maintaining feature space similarity, significantly improving classification accuracy across multiple datasets.

Contribution

Proposes BootAug, a transferable hybrid filtering framework that preserves feature space similarity, boosting the effectiveness of existing text augmentation methods on large datasets.

Findings

BootAug improves classification accuracy by approximately 2-3%.

It outperforms state-of-the-art augmentation methods on nine datasets.

Addresses performance drop caused by feature space shift in augmentation.

Abstract

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.