Disentangled Representation Learning for Non-Parallel Text Style Transfer

Vineet John, Lili Mou, Hareesh Bahuleyan, Olga Vechtomova


Abstract
This paper tackles the problem of disentangling the latent representations of style and content in language models. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for style 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 can be applied to style transfer on non-parallel corpora. We achieve high performance in terms of transfer accuracy, content preservation, and language fluency, in comparison to various previous approaches.
Anthology ID:
P19-1041
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
424–434
Language:
URL:
https://aclanthology.org/P19-1041
DOI:
10.18653/v1/P19-1041
Bibkey:
Cite (ACL):
Vineet John, Lili Mou, Hareesh Bahuleyan, and Olga Vechtomova. 2019. Disentangled Representation Learning for Non-Parallel Text Style Transfer. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 424–434, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Disentangled Representation Learning for Non-Parallel Text Style Transfer (John et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1041.pdf
Code
 vineetjohn/linguistic-style-transfer +  additional community code