Attention-based encoder-decoder model for neural machine translation
This package is based on the dl4mt-tutorial by Kyunghyun Cho et al. ( https://github.com/nyu-dl/dl4mt-tutorial ). It was used to produce top-scoring systems at the WMT 16 shared translation task.
The changes to Nematus include:
- arbitrary input features (factored neural machine translation) http://www.statmt.org/wmt16/pdf/W16-2209.pdf
- ensemble decoding (and new translation API to support it)
- dropout on all layers (Gal, 2015) http://arxiv.org/abs/1512.05287
- automatic training set reshuffling between epochs
- n-best output for decoder
- more output options (attention weights; word-level probabilities) and visualization scripts
- performance improvements to decoder
- rescoring support
- execute arbitrary validation scripts (for BLEU early stopping)
- vocabulary files and model parameters are stored in JSON format (backward-compatible loading)
Nematus requires the following packages:
- Python >= 2.7
- numpy
- ipdb
- Theano >= 0.7 (and its dependencies).
we recommend executing the following command in a Python virtual environment:
pip install numpy numexpr cython tables theano ipdb
the following packages are optional, but highly recommended
- CUDA >= 7 (only GPU training is sufficiently fast)
- cuDNN >= 3 (speeds up training substantially)
you can run Nematus locally. To install it, execute python setup.py install
You can also create docker image by running following command, where you change suffix to either cpu or gpu:
docker build -t nematus-docker -f Dockerfile.suffix .
To run a CPU docker instance with the current working directory shared with the Docker container, execute:
docker run -v `pwd`:/playground -it nematus-docker
For GPU you need to have nvidia-docker installed and run:
nvidia-docker run -v `pwd`:/playground -it nematus-docker
instructions to train a model are provided in https://github.com/rsennrich/wmt16-scripts
sample models, and instructions on using them for translation, are provided at http://statmt.org/rsennrich/wmt16_systems/
the code is based on the following model:
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio (2015): Neural Machine Translation by Jointly Learning to Align and Translate, Proceedings of the International Conference on Learning Representations (ICLR).
for the changes specific to Nematus, please consider the following papers:
Sennrich, Rico, Haddow, Barry, Birch, Alexandra (2016): Edinburgh Neural Machine Translation Systems for WMT 16, Proc. of the First Conference on Machine Translation (WMT16). Berlin, Germany
Sennrich, Rico, Haddow, Barry (2016): Linguistic Input Features Improve Neural Machine Translation, Proc. of the First Conference on Machine Translation (WMT16). Berlin, Germany