  Data augmentation (DA) aims to generate constrained and diversified data to
improve classifiers in Low-Resource Classification (LRC). Previous studies
mostly use a fine-tuned Language Model (LM) to strengthen the constraints but
ignore the fact that the potential of diversity could improve the effectiveness
of generated data. In LRC, strong constraints but weak diversity in DA result
in the poor generalization ability of classifiers. To address this dilemma, we
propose a {D}iversity-{E}nhanced and {C}onstraints-\{R}elaxed {A}ugmentation
(DECRA). Our DECRA has two essential components on top of a transformer-based
backbone model. 1) A k-beta augmentation, an essential component of DECRA, is
proposed to enhance the diversity in generating constrained data. It expands
the changing scope and improves the degree of complexity of the generated data.
2) A masked language model loss, instead of fine-tuning, is used as a
regularization. It relaxes constraints so that the classifier can be trained
with more scattered generated data. The combination of these two components
generates data that can reach or approach category boundaries and hence help
the classifier generalize better. We evaluate our DECRA on three public
benchmark datasets under low-resource settings. Extensive experiments
demonstrate that our DECRA outperforms state-of-the-art approaches by 3.8% in
the overall score.
