  Data scarcity and data imbalance have attracted a lot of attention in many
fields. Data augmentation, explored as an effective approach to tackle them,
can improve the robustness and efficiency of classification models by
generating new samples. This paper presents REPRINT, a simple and effective
hidden-space data augmentation method for imbalanced data classification. Given
hidden-space representations of samples in each class, REPRINT extrapolates, in
a randomized fashion, augmented examples for target class by using subspaces
spanned by principal components to summarize distribution structure of both
source and target class. Consequently, the examples generated would diversify
the target while maintaining the original geometry of target distribution.
Besides, this method involves a label refinement component which allows to
synthesize new soft labels for augmented examples. Compared with different NLP
data augmentation approaches under a range of data imbalanced scenarios on four
text classification benchmark, REPRINT shows prominent improvements. Moreover,
through comprehensive ablation studies, we show that label refinement is better
than label-preserving for augmented examples, and that our method suggests
stable and consistent improvements in terms of suitable choices of principal
components. Moreover, REPRINT is appealing for its easy-to-use since it
contains only one hyperparameter determining the dimension of subspace and
requires low computational resource.
