Unsupervised Statistical Machine Translation
Switch branches/tags
Nothing to show
Clone or download
Latest commit c8549e0 Oct 24, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
training Initial commit Oct 24, 2018
.gitignore Initial commit Oct 24, 2018
LICENSE.txt Initial commit Oct 24, 2018
README.md Initial commit Oct 24, 2018
get-third-party.sh Initial commit Oct 24, 2018
train.py Initial commit Oct 24, 2018
translate.py Initial commit Oct 24, 2018

README.md

Monoses

This is an open source implementation of our unsupervised statistical machine translation system, described in the following paper:

Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018. Unsupervised Statistical Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018).

If you use this software for academic research, please cite the paper in question:

@inproceedings{artetxe2018emnlp,
  author    = {Artetxe, Mikel  and  Labaka, Gorka  and  Agirre, Eneko},
  title     = {Unsupervised Statistical Machine Translation},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics}
}

Requirements

  • Python 3 with PyTorch (tested with v0.4), available from your PATH
  • Moses v4.0, compiled under third-party/moses/
  • FastAlign, compiled under third-party/fast_align/build/
  • Phrase2vec, compiled under third-party/phrase2vec/
  • VecMap, available under third-party/vecmap/

A script is provided to download all the dependencies under third-party/:

./get-third-party.sh

Note, however, that the script only downloads their source code, which you still need to compile yourself. Please refer to the original documentation of each tool for detailed instructions on how to accomplish this.

Usage

The following command trains an unsupervised SMT system from monolingual corpora using the exact same settings described in the paper:

python3 train.py --src SRC.MONO.TXT --src-lang SRC \
                 --trg TRG.MONO.TXT --trg-lang TRG \
                 --working MODEL-DIR

The parameters in the above command should be provided as follows:

  • SRC.MONO.TXT and TRG.MONO.TXT are the source and target language monolingual corpora. You should just provide the raw text, and the training script will take care of all the necessary preprocessing (tokenization, deduplication etc.).
  • SRC and TRG are the source and target language codes (e.g. 'en', 'fr', 'de'). These are used for language-specific corpus preprocessing using standard Moses tools.
  • MODEL-DIR is the directory in which to save the output model.

Using the above settings, training takes about one week in our modest server. Once training is done, you can use the resulting model for translation as follows:

python3 translate.py MODEL-DIR --src SRC --trg TRG < INPUT.TXT > OUTPUT.TXT

For more details and additional options, run the above scripts with the --help flag.

License

Copyright (C) 2018, Mikel Artetxe

Licensed under the terms of the GNU General Public License, either version 3 or (at your option) any later version. A full copy of the license can be found in LICENSE.txt.