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/
| parameter | description |
|---|---|
| --source_dataset PATH | parallel training corpus (source side) |
| --target_dataset PATH | parallel training corpus (target side) |
| --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) |
| --no_reload_training_progress | don't reload training progress (only used if --reload is enabled) |
| --summary_dir | directory for saving summaries (default: same directory as the --saveto file) |
| --summaryFreq | Save summaries after INT updates, if 0 do not save summaries (default: 0) |
| parameter | description |
|---|---|
| --embedding_size INT | embedding layer size (default: 512) |
| --state_size INT | hidden layer size (default: 1000) |
| --source_vocab_sizes INT | source vocabulary sizes (one per input factor) (default: None) |
| --target_vocab_size 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) |
| --layer_normalisation | use layer normalisation (default: False) |
| --tie_decoder_embeddings | tie the input embeddings of the decoder with the softmax output embeddings |
| --enc_depth INT | number of encoder layers (default: 1) |
| --enc_recurrence_transition_depth | number of GRU transition operations applied in an encoder layer (default: 1) |
| --dec_depth INT | number of decoder layers (default: 1) |
| --dec_base_recurrence_transition_depth | number of GRU transition operations applied in first decoder layer (default: 2) |
| --dec_high_recurrence_transition_depth | number of GRU transition operations applied in decoder layers after the first (default: 1) |
| --dec_deep_context | pass context vector (from first layer) to deep decoder layers |
| --output_hidden_activation | activation function in hidden layer of the output network (default: tanh) |
| parameter | description |
|---|---|
| --maxlen INT | maximum sequence length (default: 100) |
| --batch_size INT | minibatch size (default: 80) |
| --token_batch_size INT | minibatch size (expressed in number of source or target tokens). Sentence-level minibatch size will be dynamic. If this is enabled, batch_size only affects sorting by length. |
| --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 | MAP-L2 regularization penalty towards original weights (default: 0) |
| --prior_model STR | Prior model for MAP-L2 regularization. Unless using "--reload", this will also be used for initialization. |
| --clip_c FLOAT | gradient clipping threshold (default: 1) |
| --learning_rate FLOAT | learning rate (default: 0.0001) |
| --label_smoothing FLOAT | label smoothing (default: 0) |
| --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) |
| --optimizer | optimizer (default: adam) |
| --keep_train_set_in_memory | Keep training dataset lines stores in RAM during training |
| parameter | description |
|---|---|
| --valid_source_dataset PATH | parallel validation corpus (source side) |
| --valid_target_dataset PATH | parallel validation corpus (target side) |
| --valid_batch_size INT | validation minibatch size (default: 80) |
| --valid_token_batch_size INT | validation minibatch size (expressed in number of source or target tokens). Sentence-level minibatch size will be dynamic. If this is enabled, valid_batch_size only affects sorting by length. |
| --validFreq INT | validation frequency (default: 10000) |
| --patience INT | early stopping patience (default: 10) |
| --run_validation | Compute validation score on validation dataset |
| parameter | description |
|---|---|
| --dispFreq INT | display loss after INT updates (default: 1000) |
| --sampleFreq INT | display some samples after INT updates (default: 10000) |
| --beamFreq INT | display some beam_search samples after INT updates (default: 10000) |
| --beam_size INT | size of the beam (default: 12) |
| 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) |
| --n-best | Write n-best list (of size k) |
| 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) |
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:
| parameter | description |
|---|---|
| --input PATH, -i PATH | Input n-best list file (default: standard input) |
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).