Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation

Ning Dai, Jianze Liang, Xipeng Qiu, Xuanjing Huang


Abstract
Disentangling the content and style in the latent space is prevalent in unpaired text style transfer. However, two major issues exist in most of the current neural models. 1) It is difficult to completely strip the style information from the semantics for a sentence. 2) The recurrent neural network (RNN) based encoder and decoder, mediated by the latent representation, cannot well deal with the issue of the long-term dependency, resulting in poor preservation of non-stylistic semantic content. In this paper, we propose the Style Transformer, which makes no assumption about the latent representation of source sentence and equips the power of attention mechanism in Transformer to achieve better style transfer and better content preservation.
Anthology ID:
P19-1601
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5997–6007
Language:
URL:
https://aclanthology.org/P19-1601
DOI:
10.18653/v1/P19-1601
Bibkey:
Cite (ACL):
Ning Dai, Jianze Liang, Xipeng Qiu, and Xuanjing Huang. 2019. Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5997–6007, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (Dai et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1601.pdf
Code
 fastnlp/style-transformer +  additional community code
Data
IMDb Movie Reviews