BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework

Heng Yang; Ke Li

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arXiv · 2210.02941

BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework

Heng Yang·Ke Li

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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.

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BibTeX RIS

Heng Yang, Ke Li. 2024-04-01. BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework. https://arxiv.org/abs/2210.02941

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