  Data augmentation is widely used in text classification, especially in the
low-resource regime where a few examples for each class are available during
training. Despite the success, generating data augmentations as hard positive
examples that may increase their effectiveness is under-explored. This paper
proposes an Adversarial Word Dilution (AWD) method that can generate hard
positive examples as text data augmentations to train the low-resource text
classification model efficiently. Our idea of augmenting the text data is to
dilute the embedding of strong positive words by weighted mixing with
unknown-word embedding, making the augmented inputs hard to be recognized as
positive by the classification model. We adversarially learn the dilution
weights through a constrained min-max optimization process with the guidance of
the labels. Empirical studies on three benchmark datasets show that AWD can
generate more effective data augmentations and outperform the state-of-the-art
text data augmentation methods. The additional analysis demonstrates that the
data augmentations generated by AWD are interpretable and can flexibly extend
to new examples without further training.
