Style Transfer as Unsupervised Machine Translation

Understand

Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content.

• One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is in one style and the target sentence in another style.
• With this constraint, in this paper, we adapt unsupervised machine translation methods for the task of automatic style transfer.
• We first take advantage of style-preference information and word embedding similarity to produce pseudo-parallel data with a statistical machine translation (SMT) framework.