  Data augmentation aims to enrich training samples for alleviating the
overfitting issue in low-resource or class-imbalanced situations. Traditional
methods first devise task-specific operations such as Synonym Substitute, then
preset the corresponding parameters such as the substitution rate artificially,
which require a lot of prior knowledge and are prone to fall into the
sub-optimum. Besides, the number of editing operations is limited in the
previous methods, which decreases the diversity of the augmented data and thus
restricts the performance gain. To overcome the above limitations, we propose a
framework named Text AutoAugment (TAA) to establish a compositional and
learnable paradigm for data augmentation. We regard a combination of various
operations as an augmentation policy and utilize an efficient Bayesian
Optimization algorithm to automatically search for the best policy, which
substantially improves the generalization capability of models. Experiments on
six benchmark datasets show that TAA boosts classification accuracy in
low-resource and class-imbalanced regimes by an average of 8.8% and 9.7%,
respectively, outperforming strong baselines.
