@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://preview.aclanthology.org/fix-sig-urls/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."
}