  Data augmentation has been widely used to improve deep neural networks in
many research fields, such as computer vision. However, less work has been done
in the context of text, partially due to its discrete nature and the complexity
of natural languages. In this paper, we propose to improve the standard maximum
likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning
phase for automatic data augmentation. Unlike most existing sentence-level
augmentation strategies, which are only applied to specific models, our method
is more general and could be easily adapted to any MLE-based training
procedure. In addition, our framework allows task-specific evaluation metrics
to be designed to flexibly control the generated sentences, for example, in
terms of controlling vocabulary usage and avoiding nontrivial repetitions.
Extensive experimental results demonstrate the superiority of our method on two
synthetic and several standard real datasets, significantly improving related
baselines.
