  This paper tackles the problem of disentangling the latent variables of style
and content in language models. We propose a simple yet effective approach,
which incorporates auxiliary multi-task and adversarial objectives, for label
prediction and bag-of-words prediction, respectively. We show, both
qualitatively and quantitatively, that the style and content are indeed
disentangled in the latent space. This disentangled latent representation
learning method is applied to style transfer on non-parallel corpora. We
achieve substantially better results in terms of transfer accuracy, content
preservation and language fluency, in comparison to previous state-of-the-art
approaches.
