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:

• the model has been re-implemented in tensorflow. See https://github.com/EdinburghNLP/nematus/tree/theano for the Theano-based version of Nematus.

• new architecture variants for better performance:

  • arbitrary input features (factored neural machine translation) http://www.statmt.org/wmt16/pdf/W16-2209.pdf
  • deep models (Miceli Barone et al., 2017) https://arxiv.org/abs/1707.07631
  • dropout on all layers (Gal, 2015) http://arxiv.org/abs/1512.05287
  • tied embeddings (Press and Wolf, 2016) https://arxiv.org/abs/1608.05859
  • layer normalisation (Ba et al, 2016) https://arxiv.org/abs/1607.06450

• improvements to scoring and decoding:

  • n-best output for decoder
  • scripts for scoring (given parallel corpus) and rescoring (of n-best output)

• usability improvements:

  • command line interface for training
  • vocabulary files and model parameters are stored in JSON format (backward-compatible loading)
  • server mode

see changelog for more info.

For general support requests, there is a Google Groups mailing list at https://groups.google.com/d/forum/nematus-support . You can also send an e-mail to nematus-support@googlegroups.com .

Nematus requires the following packages:

• Python >= 2.7
• tensorflow

To install tensorflow, we recommend following the steps at: ( https://www.tensorflow.org/install/ )

the following packages are optional, but highly recommended

• CUDA >= 7 (only GPU training is sufficiently fast)
• cuDNN >= 4 (speeds up training substantially)

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

Training speed depends heavily on having appropriate hardware (ideally a recent NVIDIA GPU), and having installed the appropriate software packages.

To test your setup, we provide some speed benchmarks with `test/test_train.sh', on an Intel Xeon CPU E5-2620 v4, with a Nvidia GeForce GTX Titan X (Pascal) and CUDA 9.0:

GPU, CuDNN 5.1, tensorflow 1.0.1:

CUDA_VISIBLE_DEVICES=0 ./test_train.sh

225.25 sentenses/s

All of the scripts below can be run with --help flag to get usage information.

Sample commands with toy examples are available in the test directory; for training a full-scale system, consider the training scripts at http://data.statmt.org/wmt17_systems/training/

The n-best list is assumed to have the same format as Moses:

sentence-ID (starting from 0) ||| translation ||| scores

new scores will be appended to the end. rescore.py has the same arguments as score.py, with the exception of this additional parameter:

If you have a Theano model (model.npz) with network architecture features that are currently supported then you can convert it into a tensorflow model using nematus/theano_tf_convert.py.

if you use Nematus, please cite the following paper:

Rico Sennrich, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry and Maria Nadejde (2017): Nematus: a Toolkit for Neural Machine Translation. In Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, pp. 65-68.

@InProceedings{sennrich-EtAl:2017:EACLDemo,
  author    = {Sennrich, Rico  and  Firat, Orhan  and  Cho, Kyunghyun  and  Birch, Alexandra  and  Haddow, Barry  and  Hitschler, Julian  and  Junczys-Dowmunt, Marcin  and  L\"{a}ubli, Samuel  and  Miceli Barone, Antonio Valerio  and  Mokry, Jozef  and  Nadejde, Maria},
  title     = {Nematus: a Toolkit for Neural Machine Translation},
  booktitle = {Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {65--68},
  url       = {http://aclweb.org/anthology/E17-3017}
}

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).

please refer to the Nematus paper for a description of implementation differences

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements 645452 (QT21), 644333 (TraMOOC), 644402 (HimL) and 688139 (SUMMA).