Parallel Data Augmentation for Formality Style Transfer
This work addresses a data bottleneck in formality style transfer for natural language processing applications, though it is incremental as it focuses on data augmentation rather than a new model.
The paper tackled the problem of insufficient training data for Formality Style Transfer by proposing simple data augmentation methods to generate useful parallel sentence pairs, achieving state-of-the-art results on the GYAFC benchmark dataset.
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.