This code corresponding to the paper: DGST: a Dual-Generator Network for Text Style Transfer (EMNLP2020).

The main website is here https://xiao.ac/proj/dgst.

This code is based on

python 3.7
pytorch (version >= 1.4.0)
torchvision (version >= 0.4.1)
fasttext (version >= 0.8.4)
nltk
tqmd

Please run Exp_DGST.py to train the model like:

> python3 Exp_DGST.py --dataset Yelp

Or

> python3 Exp_DGST.py --dataset Imdb

to train DEST model on Yelp or Imdb dataset.

You can use the parameter -pg to show the training progress, e.g.

> python3 Exp_DGST.py --dataset Yelp -pg

The trained model will be saved in ./model_save/ , and the outcomes will be in ./outputs/ .

There are ablation study types named:

1. full-model
2. no-rec
3. no-tran
4. rec-no-noise
5. tran-no-noise
6. pre-noise

For details please see the paper.

To run the ablation study, just run the file Exp_ablation_study.py .

> python3 Exp_ablation_study.py

Then the file will let you choose a ablation type.

You can also use the parameter -pg if you want to show the training progress, e.g.

> python3 Exp_ablation_study.py -pg

This work has been published in EMNLP2020. Here is the paper. If you find MSP interesting, please consider citing:

  @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", 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://www.aclweb.org/anthology/2020.emnlp-main.578", }  

This work is supported by the award made by the UK Engineering and Physical SciencesResearch Council (Grant number: EP/P011829/1).