Language style transfer is the problem of migrating the content of a source
sentence to a target style. In many of its applications, parallel training data
are not available and source sentences to be transferred may have arbitrary and
unknown styles. First, each sentence is encoded into its content and style
latent representations. Then, by recombining the content with the target style,
we decode a sentence aligned in the target domain. To adequately constrain the
encoding and decoding functions, we couple them with two loss functions. The
first is a style discrepancy loss, enforcing that the style representation
accurately encodes the style information guided by the discrepancy between the
sentence style and the target style. The second is a cycle consistency loss,
which ensures that the transferred sentence should preserve the content of the
original sentence disentangled from its style. We validate the effectiveness of
our model in three tasks: sentiment modification of restaurant reviews, dialog
response revision with a romantic style, and sentence rewriting with a
Shakespearean style.

该研究论文介绍了一种利用内容和风格潜在表示进行语言风格转换的方法，并证明了该模型在餐厅评论情感修改、浪漫风格对话回复和莎士比亚风格语句重写等三种任务中具有有效性。