  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.
Then the iterative back-translation approach is employed to jointly train two
neural machine translation (NMT) based transfer systems. To control the noise
generated during joint training, a style classifier is introduced to guarantee
the accuracy of style transfer and penalize bad candidates in the generated
pseudo data. Experiments on benchmark datasets show that our proposed method
outperforms previous state-of-the-art models in terms of both accuracy of style
transfer and quality of input-output correspondence.
