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    <abstract>Most previous methods for text data augmentation are limited to simple tasks and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot natural language understanding) and strong baselines (i.e., pretrained models with over one billion parameters). Under this setting, we reproduced a large number of previous augmentation methods and found that these methods bring marginal gains at best and sometimes degrade the performance much. To address this challenge, we propose a novel data augmentation method FlipDA that jointly uses a generative model and a classifier to generate label-flipped data. Central to the idea of FlipDA is the discovery that generating label-flipped data is more crucial to the performance than generating label-preserved data. Experiments show that FlipDA achieves a good tradeoff between effectiveness and robustness—it substantially improves many tasks while not negatively affecting the others.</abstract>
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%0 Conference Proceedings
%T FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
%A Zhou, Jing
%A Zheng, Yanan
%A Tang, Jie
%A Jian, Li
%A Yang, Zhilin
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhou-etal-2022-flipda
%X Most previous methods for text data augmentation are limited to simple tasks and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot natural language understanding) and strong baselines (i.e., pretrained models with over one billion parameters). Under this setting, we reproduced a large number of previous augmentation methods and found that these methods bring marginal gains at best and sometimes degrade the performance much. To address this challenge, we propose a novel data augmentation method FlipDA that jointly uses a generative model and a classifier to generate label-flipped data. Central to the idea of FlipDA is the discovery that generating label-flipped data is more crucial to the performance than generating label-preserved data. Experiments show that FlipDA achieves a good tradeoff between effectiveness and robustness—it substantially improves many tasks while not negatively affecting the others.
%R 10.18653/v1/2022.acl-long.592
%U https://aclanthology.org/2022.acl-long.592
%U https://doi.org/10.18653/v1/2022.acl-long.592
%P 8646-8665

Markdown (Informal)

[FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://aclanthology.org/2022.acl-long.592) (Zhou et al., ACL 2022)

• FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning (Zhou et al., ACL 2022)

ACL

• Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, and Zhilin Yang. 2022. FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8646–8665, Dublin, Ireland. Association for Computational Linguistics.