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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:

• arbitrary input features (factors)
• 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

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