DeepLearnXMU / VNMT

Code for "Variational Neural Machine Translation" (EMNLP2016)

VNMT

Current implementation for VNMT only support 1 layer NMT! Deep layers are meaningless.

Source code for variational neural machine translation, we will make it available soon!

If you use this code, please cite our paper:

@InProceedings{zhang-EtAl:2016:EMNLP20162,
  author    = {Zhang, Biao  and  Xiong, Deyi  and  su, jinsong  and  Duan, Hong  and  Zhang, Min},
  title     = {Variational Neural Machine Translation},
  booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
  month     = {November},
  year      = {2016},
  address   = {Austin, Texas},
  publisher = {Association for Computational Linguistics},
  pages     = {521--530},
  url       = {https://aclweb.org/anthology/D16-1050}
}

Basic Requirement

Our source is based on the GroundHog. Before use our code, please install it.

How to Run?

To train a good VNMT model, you need follow two steps.

Step 1. Pretraining

Pretrain a base model using the GroundHog.

Step 2. Retraining

Go to the work directory, and put the pretrained model to this directory, i.e. use the pretrained model to initialize the parameters of VNMT.

Simply Run (Clearly, before that you need re-config the chinese.py file to your own dataset :))

run.sh

That's it!

Notice that our test and deveopment set is the NIST dataset, which follow the sgm format! Please see the work/data/dev for example.

For any comments or questions, please email Biao Zhang.