Tested with Python 2.7.3 and Tensorflow 1.1
Talk: https://www3.nd.edu/~tnguye28/naacl18.pdf
General
This is the code for the paper Improving Lexical Choice in Neural Machine Translation (accepted at NAACL HLT 2018). The branches are:
- master: baseline NMT
- tied_embedding: baseline NMT with tied embedding
- fixnorm: fixnorm model in paper
- fixnorm_lex: fixnorm+lex model in paper
- arthur: apply the method of Arthur et al. on top of tied_embedding NMT
To train a model:
- write a configuration function in
configurations.py - run:
python -m nmt --proto your_config_func
Depending on your config function, the code generates a direction under nmt/saved_models/your_model_name and saves all dev validations there, as well as dev perplexities, train perplexities, best model checkpoint, checkpoint so far (I've tested with saving 1 best checkpoint, not sure about > 1). You should use this checkpoint to translate on any other input.
To translate with UNK replacement:
- run:
python -m nmt --proto your_config_func --mode translate --unk-repl --model-file path_your_saved_checkpoint.cpkt --input-file path_to_input_file
Remember the checkpoint includes data file, meta file, ... but just link to .cpkt, ignore the extension.
References
Code & scripts might be inspired/borrowed from some sources: