Structured Content Preservation for Unsupervised Text Style Transfer

Understand

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.