Skip to content
master
Go to file
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 

README.md

DeconvDec

Code for the model proposed in our paper Deconvolution-Based Global Decoding for Neural Machine Translation, http://aclweb.org/anthology/C18-1276.

Requirements

  • Ubuntu 16.0.4
  • Python 3.5
  • Pytorch 0.4.1

Preprocessing

python3 preprocess.py -load_data path_to_data -save_data path_to_store_data 

Remember to put the data into a folder and name them train.src, train.tgt, valid.src, valid.tgt, test.src and test.tgt, and make a new folder inside called data. For more detailed setting, check the options in the file.


Training

python3 train.py -log log_name -config config_yaml -gpus id

Create your own yaml file for hyperparameter setting.


Evaluation

python3 train.py -log log_name -config config_yaml -gpus id -restore checkpoint -mode eval

Citation

If you use this code for your research, please kindly cite our paper:.

@inproceedings{DeconvDec,
  author    = {Junyang Lin and
               Xu Sun and
               Xuancheng Ren and
               Shuming Ma and
               Jinsong Su and
               Qi Su},
  title     = {Deconvolution-Based Global Decoding for Neural Machine Translation},
  booktitle = {Proceedings of the 27th International Conference on Computational
               Linguistics, {COLING} 2018, Santa Fe, New Mexico, USA, August 20-26,
               2018},
  pages     = {3260--3271},
  year      = {2018}
}

About

Code for "Deconvolution-Based Global Decoding for Neural Machine Translation" (COLING 2018).

Resources

License

Releases

No releases published

Packages

No packages published
You can’t perform that action at this time.