  Text style transfer aims to modify the style of a sentence while keeping its
content unchanged. Recent style transfer systems often fail to faithfully
preserve the content after changing the style. This paper proposes a structured
content preserving model that leverages linguistic information in the
structured fine-grained supervisions to better preserve the style-independent
content during style transfer. In particular, we achieve the goal by devising
rich model objectives based on both the sentence's lexical information and a
language model that conditions on content. The resulting model therefore is
encouraged to retain the semantic meaning of the target sentences. We perform
extensive experiments that compare our model to other existing approaches in
the tasks of sentiment and political slant transfer. Our model achieves
significant improvement in terms of both content preservation and style
transfer in automatic and human evaluation.
