Tested with Python 2.7.3 and Tensorflow 1.1

What does it do exactly?

It trains a global attentional NMT model based on Effective Approaches to Attention-based Neural Machine Translation using beam search with length normalization by Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

It trains with negative loglikelihood objective and periodically evaluates the performance (BLEU score) on dev set. It can save a few best models as defined in the configuration function. I always save one best model (highest BLEU score on the dev set).

How to use this code?

• Put your data including {train,dev,test}.{source,target} into a folder, let's call it s2t, in the nmt/data directory
• Write corresponding configuration function in configurations.py, let's call it s2t_config
• To train, run python -m nmt --proto s2t_config
• To translate (with unk replacement) a file with saved model, run python -m nmt --mode translate --unk-repl --proto s2t_config --model-file nmt/saved_models/your_saved_model_dir_name/your_model_name-best-bleu-score.cpkt --input-file path_to_file_to_translate

An example configuration function test_en2vi is provided.

How to train from a pretrained model?

There's a reload option. If this is set to True, and if the code sees a corresponding checkpoint it will automatically start training from that checkpoint.

How to freeze some parameters during training?

For example, if we want to not train the output embedding, we can access this from our model as model.softmax.logit_layer.W, so we fix it this way:

python -m nmt --proto s2t --fixed-var-list softmax.logit_layer.W

References

I was inspired by code from lots of examples out there I can't start to remember. A lot of code is referenced from Blocks examples, DL4MT, and multi-bleu.perl is taken from Moses.

We use this code for the paper Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation which will appear at IJCNLP'17. We use the code at subword-nmt for processing BPE as explained in the paper.