FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning

• URL: http://arxiv.org/abs/2108.06332v1
• Date: Fri, 13 Aug 2021 17:51:31 GMT
• Title: FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
• Authors: Jing Zhou, Yanan Zheng, Jie Tang, Jian Li, Zhilin Yang
• Abstract summary: We propose a novel data augmentation method FlipDA that jointly uses a generative model and a classifier to generate label-flipped data. Experiments show that FlipDA achieves a good tradeoff between effectiveness and robustness---it substantially improves many tasks while not negatively affecting the others.
• Score: 27.871007011425775
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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