Structured Content Preservation for Unsupervised Text Style Transfer

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. 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. Text style transfer is an important task in designing sophisticated and controllable natural language generation (NLG) systems. The goal of this task is to convert a sentence from one style (e.g., negative sentiment) to another (e.g., positive sentiment), while preserving the style-independent content (e.g., the name of the food being discussed). Typically, it is difficult to find parallel data with different styles. So we must learn to disentangle the representations of the style from the content. However, it is impossible to separate the two components by simply adding or dropping certain words.