Open-Source Neural Machine Translation in Theano
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This fork of Nematus contains the code for: Neural Lattice Search for Domain Adaptation in Machine Translation Huda Khayrallah Gaurav Kumar Kevin Duh Matt Post Philipp Koehn {huda, gkumar, kevinduh, post, phi}

USAGE: decoding can be perfomed with an existing nematus model.

Rescoring of a Moses word graph using a translation model is performed with: nematus/ The Moses word graph is obtained by adding the flags "-osg " to Moses. It then must be converted to fst format using: scripts/

Attention-based encoder-decoder model for neural machine translation

This package is based on the dl4mt-tutorial by Kyunghyun Cho et al. ( ). 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)
  • ensemble decoding (and new translation API to support it)
  • dropout on all layers (Gal, 2015)
  • minimum risk training (Shen et al, 2016)
  • tied embeddings (Press and Wolf, 2016)
  • command line interface for training
  • 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)


For general support requests, there is a Google Groups mailing list at . You can also send an e-mail to .


Nematus requires the following packages:

  • Python >= 2.7
  • numpy
  • Theano >= 0.7 (and its dependencies).

we recommend executing the following command in a Python virtual environment: pip install numpy numexpr cython tables theano

the following packages are optional, but highly recommended

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

you can run Nematus locally. To install it, execute python 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


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/', on an Intel Xeon CPU E5-2620 v3, with a Nvidia GeForce GTX 1080 GPU and CUDA 8.0:

CPU, theano 0.8.2:

THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=cpu ./

2.37 sentences/s

GPU, no CuDNN, theano 0.8.2:

THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./

71.62 sentences/s

GPU, CuDNN 5.1, theano 0.8.2:

THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./

139.73 sentences/s

GPU, CuDNN 5.1, theano

THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=gpu ./

173.15 sentences/s

GPU, CuDNN 5.1, theano, new GPU backend:

THEANO_FLAGS=mode=FAST_RUN,floatX=float32,device=cuda ./

209.21 sentences/s


execute nematus/ to train a model.

data sets; model loading and saving

parameter description
--datasets PATH PATH parallel training corpus (source and target)
--dictionaries PATH [PATH ...] network vocabularies (one per source factor, plus target vocabulary)
--model PATH model file name (default: model.npz)
--saveFreq INT save frequency (default: 30000)
--reload load existing model (if '--model' points to existing model)
--overwrite write all models to same file

network parameters

parameter description
--dim_word INT embedding layer size (default: 512)
--dim INT hidden layer size (default: 1000)
--n_words_src INT source vocabulary size (default: None)
--n_words INT target vocabulary size (default: None)
--factors INT number of input factors (default: 1)
--dim_per_factor INT [INT ...] list of word vector dimensionalities (one per factor): '--dim_per_factor 250 200 50' for total dimensionality of 500 (default: None)
--use_dropout use dropout layer (default: False)
--dropout_embedding FLOAT dropout for input embeddings (0: no dropout) (default: 0.2)
--dropout_hidden FLOAT dropout for hidden layer (0: no dropout) (default: 0.2)
--dropout_source FLOAT dropout source words (0: no dropout) (default: 0)
--dropout_target FLOAT dropout target words (0: no dropout) (default: 0)
--tie_decoder_embeddings tie the input embeddings of the decoder with the softmax output embeddings
--tie_encoder_decoder_embeddings tie the input embeddings of the encoder and the decoder (first factor only). Source and target vocabulary size must the same

training parameters

parameter description
--maxlen INT maximum sequence length (default: 100)
--optimizer {adam,adadelta,rmsprop,sgd} optimizer (default: adam)
--batch_size INT minibatch size (default: 80)
--max_epochs INT maximum number of epochs (default: 5000)
--finish_after INT maximum number of updates (minibatches) (default: 10000000)
--decay_c FLOAT L2 regularization penalty (default: 0)
--map_decay_c FLOAT L2 regularization penalty towards original weights (default: 0)
--alpha_c FLOAT alignment regularization (default: 0)
--clip_c FLOAT gradient clipping threshold (default: 1)
--lrate FLOAT learning rate (default: 0.0001)
--no_shuffle disable shuffling of training data (for each epoch)
--no_sort_by_length do not sort sentences in maxibatch by length
--maxibatch_size INT size of maxibatch (number of minibatches that are sorted by length) (default: 20)
--objective {CE,MRT} training objective. CE: cross-entropy minimization (default); MRT: Minimum Risk Training (

validation parameters

parameter description
--valid_datasets PATH PATH parallel validation corpus (source and target)
--valid_batch_size INT validation minibatch size (default: 80)
--validFreq INT validation frequency (default: 10000)
--patience INT early stopping patience (default: 10)
--external_validation_script PATH location of validation script (to run your favorite metric for validation) (default: None)

display parameters

parameter description
--dispFreq INT display loss after INT updates (default: 1000)
--sampleFreq INT display some samples after INT updates (default: 10000)

minimum risk training parameters

parameter description
--mrt_alpha FLOAT MRT alpha (default: 0.005)
--mrt_samples INT samples per source sentence (default: 100)
--mrt_samples_meanloss INT draw n independent samples to calculate mean loss (which is subtracted from loss) (default: 10)
--mrt_loss STR loss used in MRT (default: SENTENCEBLEU n=4)
--mrt_reference add reference to MRT samples.
--mrt_ml_mix mix in ML objective in MRT training with this scaling factor (default: 0)

more instructions to train a model, including a sample configuration and preprocessing scripts, are provided in


nematus/ : use an existing model to translate a source text

parameter description
-k K Beam size (default: 5))
-p P Number of processes (default: 5))
-n Normalize scores by sentence length
-v verbose mode.
--models MODELS [MODELS ...], -m MODELS [MODELS ...] model to use. Provide multiple models (with same vocabulary) for ensemble decoding
--input PATH, -i PATH Input file (default: standard input)
--output PATH, -o PATH Output file (default: standard output)
--output_alignment PATH, -a PATH Output file for alignment weights (default: standard output)
--json_alignment Output alignment in json format
--n-best Write n-best list (of size k)
--suppress-unk Suppress hypotheses containing UNK.
--print-word-probabilities, -wp Print probabilities of each word
--search_graph, -sg Output file for search graph rendered as PNG image

nematus/ : use an existing model to score a parallel corpus

parameter description
-b B Minibatch size (default: 80))
-n Normalize scores by sentence length
-v verbose mode.
--models MODELS [MODELS ...], -m MODELS [MODELS ...] model to use. Provide multiple models (with same vocabulary) for ensemble decoding
--source PATH, -s PATH Source text file
--target PATH, -t PATH Target text file
--output PATH, -o PATH Output file (default: standard output)
--walign, -w Whether to store the alignment weights or not. If specified, weights will be saved in .alignment

nematus/ : use an existing model to rescore an n-best list.

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. has the same arguments as, with the exception of this additional parameter:

parameter description
--input PATH, -i PATH Input n-best list file (default: standard input)

sample models, and instructions on using them for translation, are provided in the test directory, and at


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 Demonstrations at the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain.

@inproceedings{nematus, address = "Valencia, Spain", 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", booktitle = "{Proceedings of the Demonstrations at the 15th Conference of the European Chapter of the Association for Computational Linguistics}", title = "{Nematus: a Toolkit for Neural Machine Translation}", year = "2017" }

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).