  Data augmentation is an effective way to improve the performance of many
neural text generation models. However, current data augmentation methods need
to define or choose proper data mapping functions that map the original samples
into the augmented samples. In this work, we derive an objective to formulate
the problem of data augmentation on text generation tasks without any use of
augmented data constructed by specific mapping functions. Our proposed
objective can be efficiently optimized and applied to popular loss functions on
text generation tasks with a convergence rate guarantee. Experiments on five
datasets of two text generation tasks show that our approach can approximate or
even surpass popular data augmentation methods.
