  Efforts to leverage deep learning models in low-resource regimes have led to
numerous augmentation studies. However, the direct application of methods such
as mixup and cutout to text data, is limited due to their discrete
characteristics. While methods using pretrained language models have exhibited
efficiency, they require additional considerations for robustness. Inspired by
recent studies on decision boundaries, this paper proposes a
decision-boundary-aware data augmentation strategy to enhance robustness using
pretrained language models. The proposed technique first focuses on shifting
the latent features closer to the decision boundary, followed by reconstruction
to generate an ambiguous version with a soft label. Additionally, mid-K
sampling is suggested to enhance the diversity of the generated sentences. This
paper demonstrates the performance of the proposed augmentation strategy
compared to other methods through extensive experiments. Furthermore, the
ablation study reveals the effect of soft labels and mid-K sampling and the
extensibility of the method with curriculum data augmentation.
