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
this https URL

本文提出了一种基于双重强化学习框架的一步映射模型，以直接传输文本的风格，而无需分离内容和风格，通过在双重结构上设计的两种奖励来反映风格准确性和内容保留。这种方法可以在没有使用并行数据的情况下通过强化学习训练出两个一步映射模型，评估表明该模型在两个基准数据集上表现优于现有技术，特别是 BLEU 分数平均提高了 8 分以上，人类评估也验证了该模型在风格准确性、内容保留和流畅性方面的有效性。