@inproceedings{li-etal-2020-dgst,
title = "{DGST}: a Dual-Generator Network for Text Style Transfer",
author = "Li, Xiao and
Chen, Guanyi and
Lin, Chenghua and
Li, Ruizhe",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.578/",
doi = "10.18653/v1/2020.emnlp-main.578",
pages = "7131--7136",
abstract = "We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs."
}
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<abstract>We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs.</abstract>
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%0 Conference Proceedings
%T DGST: a Dual-Generator Network for Text Style Transfer
%A Li, Xiao
%A Chen, Guanyi
%A Lin, Chenghua
%A Li, Ruizhe
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-dgst
%X We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs.
%R 10.18653/v1/2020.emnlp-main.578
%U https://aclanthology.org/2020.emnlp-main.578/
%U https://doi.org/10.18653/v1/2020.emnlp-main.578
%P 7131-7136
Markdown (Informal)
[DGST: a Dual-Generator Network for Text Style Transfer](https://aclanthology.org/2020.emnlp-main.578/) (Li et al., EMNLP 2020)
ACL
- Xiao Li, Guanyi Chen, Chenghua Lin, and Ruizhe Li. 2020. DGST: a Dual-Generator Network for Text Style Transfer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7131–7136, Online. Association for Computational Linguistics.