$ cd ./Scripts/Experiments/

$ cd ./Scripts/Model/
$ python3 model_wngt.py --corpus <name_of_corpus> --C <value of C> --checkpoint <save_model_file> --is_load 0 --model <model to train: e.g. LSTM-LSTM>

$ python3 model_wngt.py --help

@inproceedings{prokhorov-etal-2019-importance,
    title = "On the Importance of the {K}ullback-{L}eibler Divergence Term in Variational Autoencoders for Text Generation",
    author = "Prokhorov, Victor  and
      Shareghi, Ehsan  and
      Li, Yingzhen  and
      Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5612",
    doi = "10.18653/v1/D19-5612",
    pages = "118--127",
    abstract = "Variational Autoencoders (VAEs) are known to suffer from learning uninformative latent representation of the input due to issues such as approximated posterior collapse, or entanglement of the latent space. We impose an explicit constraint on the Kullback-Leibler (KL) divergence term inside the VAE objective function. While the explicit constraint naturally avoids posterior collapse, we use it to further understand the significance of the KL term in controlling the information transmitted through the VAE channel. Within this framework, we explore different properties of the estimated posterior distribution, and highlight the trade-off between the amount of information encoded in a latent code during training, and the generative capacity of the model.",
}