AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods
  in Low-resource Regimes
Juhwan Choi Kyohoon Jin Junho Lee Sangmin Song Youngbin Kim
Text data augmentation is a complex problem due to the discrete nature of
sentences. Although rule-based augmentation methods are widely adopted in
real-world applications because of their simplicity, they suffer from potential
semantic damage. Previous researchers have suggested easy data augmentation
with soft labels (softEDA), employing label smoothing to mitigate this problem.
However, finding the best factor for each model and dataset is challenging;
therefore, using softEDA in real-world applications is still difficult. In this
paper, we propose adapting AutoAugment to solve this problem. The experimental
results suggest that the proposed method can boost existing augmentation
methods and that rule-based methods can enhance cutting-edge pre-trained
language models. We offer the source code.