  Text style transfer aims to alter the style (e.g., sentiment) of a sentence
while preserving its content. A common approach is to map a given sentence to
content representation that is free of style, and the content representation is
fed to a decoder with a target style. Previous methods in filtering style
completely remove tokens with style at the token level, which incurs the loss
of content information. In this paper, we propose to enhance content
preservation by implicitly removing the style information of each token with
reverse attention, and thereby retain the content. Furthermore, we fuse content
information when building the target style representation, making it dynamic
with respect to the content. Our method creates not only style-independent
content representation, but also content-dependent style representation in
transferring style. Empirical results show that our method outperforms the
state-of-the-art baselines by a large margin in terms of content preservation.
In addition, it is also competitive in terms of style transfer accuracy and
fluency.
