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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:

• 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
• 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

Change the hard-coded paths to data in test/train_nmt.py then run

THEANO_FLAGS=device=gpu,floatX=float32 python train_nmt.py 

To decode, see the command test/test.sh

A sample setup that includes preprocessing is provided at https://github.com/rsennrich/wmt16-scripts/tree/master/sample

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:

TBD