Description

Implementation of "Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation".

This work is based on OpenNMT, an open-source (MIT) neural machine translation system. We did modification by integrating sense information.

Quickstart

1) Preprocessing.

python preprocess.py -train_src train.tok.$lsource -train_tgt train.tok.$ltarget -src_vocab src.dict -tgt_vocab tgt.dict -feature_vocab feature.dict -valid_src dev.tok.$lsource -valid_tgt dev.tok.$ltarget -save_data $savePath''demo -feature 1 -lower

2) Train the model.

python train.py -data $savePath''demo.train.pt -pre_feature_vecs_enc feature_embed.dict -pre_word_vecs_enc src_embed.dict -save_model model -gpus 0 -epochs  -brnn 

3) Translate sentences.

python translate.py -model model.pt -src $testPath''test.tok.$lsource -replace_unk -verbose -output test.$ltarget -gpu 0