  Unsupervised text style transfer aims to transfer the underlying style of
text but keep its main content unchanged without parallel data. Most existing
methods typically follow two steps: first separating the content from the
original style, and then fusing the content with the desired style. However,
the separation in the first step is challenging because the content and style
interact in subtle ways in natural language. Therefore, in this paper, we
propose a dual reinforcement learning framework to directly transfer the style
of the text via a one-step mapping model, without any separation of content and
style. Specifically, we consider the learning of the source-to-target and
target-to-source mappings as a dual task, and two rewards are designed based on
such a dual structure to reflect the style accuracy and content preservation,
respectively. In this way, the two one-step mapping models can be trained via
reinforcement learning, without any use of parallel data. Automatic evaluations
show that our model outperforms the state-of-the-art systems by a large margin,
especially with more than 8 BLEU points improvement averaged on two benchmark
datasets. Human evaluations also validate the effectiveness of our model in
terms of style accuracy, content preservation and fluency. Our code and data,
including outputs of all baselines and our model are available at
https://github.com/luofuli/DualLanST.
