  Style transfer is the task of rephrasing the text to contain specific
stylistic properties without changing the intent or affect within the context.
This paper introduces a new method for automatic style transfer. We first learn
a latent representation of the input sentence which is grounded in a language
translation model in order to better preserve the meaning of the sentence while
reducing stylistic properties. Then adversarial generation techniques are used
to make the output match the desired style. We evaluate this technique on three
different style transformations: sentiment, gender and political slant.
Compared to two state-of-the-art style transfer modeling techniques we show
improvements both in automatic evaluation of style transfer and in manual
evaluation of meaning preservation and fluency.
