Parallel Data Augmentation for Formality Style Transfer
Yi Zhang, Tao Ge, Xu Sun
Abstract
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 1 .
Topics & Concepts
FormalityComputer scienceTask (project management)Benchmark (surveying)SentenceStyle (visual arts)Transfer (computing)Data modelingSimple (philosophy)Artificial intelligenceNatural language processingDatabaseParallel computingEngineeringArchaeologyLinguisticsSystems engineeringEpistemologyGeodesyPhilosophyHistoryGeographyNatural Language Processing TechniquesTopic ModelingSpeech Recognition and Synthesis