  Few-shot text classification is a fundamental NLP task in which a model aims
to classify text into a large number of categories, given only a few training
examples per category. This paper explores data augmentation -- a technique
particularly suitable for training with limited data -- for this few-shot,
highly-multiclass text classification setting. On four diverse text
classification tasks, we find that common data augmentation techniques can
improve the performance of triplet networks by up to 3.0% on average.
  To further boost performance, we present a simple training strategy called
curriculum data augmentation, which leverages curriculum learning by first
training on only original examples and then introducing augmented data as
training progresses. We explore a two-stage and a gradual schedule, and find
that, compared with standard single-stage training, curriculum data
augmentation trains faster, improves performance, and remains robust to high
amounts of noising from augmentation.
