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.
John et al. REF proposed a disentangled representation learning approach, using auxiliary loss and adversarial learning to enforce the separation of style and content latent spaces.
Disentangled Representation Learning for Non-Parallel Text Style Transfer
ACL 2019
arXiv: Computation and Language
Computer Science