  The main barrier to progress in the task of Formality Style Transfer is the
inadequacy of training data. In this paper, we study how to augment parallel
data and propose novel and simple data augmentation methods for this task to
obtain useful sentence pairs with easily accessible models and systems.
Experiments demonstrate that our augmented parallel data largely helps improve
formality style transfer when it is used to pre-train the model, leading to the
state-of-the-art results in the GYAFC benchmark dataset.
