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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 approximately 2% in aspect-based sentiment classification). To address this problem, we propose a hybrid instance-filtering framework (BoostAug) based on pre-trained language models that can maintain a similar feature space with natural datasets. BoostAug is transferable to existing text augmentation methods (such as synonym substitution and back translation) and significantly improves the augmentation performance by 2-3% in classification accuracy. Our experimental results on three classification tasks and nine public datasets show that BoostAug 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.</abstract>
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%0 Conference Proceedings
%T Boosting Text Augmentation via Hybrid Instance Filtering Framework
%A Yang, Heng
%A Li, Ke
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yang-li-2023-boosting
%X 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 approximately 2% in aspect-based sentiment classification). To address this problem, we propose a hybrid instance-filtering framework (BoostAug) based on pre-trained language models that can maintain a similar feature space with natural datasets. BoostAug is transferable to existing text augmentation methods (such as synonym substitution and back translation) and significantly improves the augmentation performance by 2-3% in classification accuracy. Our experimental results on three classification tasks and nine public datasets show that BoostAug 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.
%R 10.18653/v1/2023.findings-acl.105
%U https://aclanthology.org/2023.findings-acl.105
%U https://doi.org/10.18653/v1/2023.findings-acl.105
%P 1652-1669

Markdown (Informal)

[Boosting Text Augmentation via Hybrid Instance Filtering Framework](https://aclanthology.org/2023.findings-acl.105) (Yang & Li, Findings 2023)

• Boosting Text Augmentation via Hybrid Instance Filtering Framework (Yang & Li, Findings 2023)

ACL

• Heng Yang and Ke Li. 2023. Boosting Text Augmentation via Hybrid Instance Filtering Framework. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1652–1669, Toronto, Canada. Association for Computational Linguistics.