• Python 3
• PyTorch (tested with v0.3)
• NLTK

Order of pre-processing should be:

• bash prep_eu_wiki.sh
• bash prep_en_data.sh
• bash download_embeddings.sh
• python extract_vocab.py cc.eu.300.vec vocab.eu train_vocab.eu
• python extract_vocab.py wiki-news-300d-1M.vec vocab.en train_vocab.en
• python update_corpus.py sentences.en train_vocab.en train_sentences.en
• python update_corpus.py sentences.eu train_vocab.eu train_sentences.eu

Then to use vecmap we need to git clone it

• cd vecmap
• nohup python3 map_embeddings,py --semi_supervised ../undreamt/train_dict.txt ../undreamt/train_emb.en ../undreamt/train_emb.eu ../undreamt/train_emb_mapped.en ../undreamt/train_emb_mapped.eu > out.log &

The following command trains an unsupervised NMT system from monolingual corpora using the exact same settings described in the paper:

python3 train.py --src SRC.MONO.TXT --trg TRG.MONO.TXT --src_embeddings SRC.EMB.TXT --trg_embeddings TRG.EMB.TXT --save MODEL_PREFIX --cuda

The data in the above command should be provided as follows:

• SRC.MONO.TXT and TRG.MONO.TXT are the source and target language monolingual corpora. They should both be pre-processed so atomic symbols (either tokens or BPE units) are separated by whitespaces. For that purpose, we recommend using Moses to tokenize and truecase the corpora and, optionally, Subword-NMT if you want to use BPE.
• SRC.EMB.TXT and TRG.EMB.TXT are the source and target language cross-lingual embeddings. In order to obtain them, we recommend training monolingual embeddings in the corpora above using either word2vec or fasttext, and then map them to a shared space using VecMap. Please make sure to cutoff the vocabulary as desired before mapping the embeddings.
• MODEL_PREFIX is the prefix of the output model.

Using the above settings, training takes about 3 days in a single Titan Xp. Once training is done, you can use the resulting model for translation as follows:

python3 translate.py MODEL_PREFIX.final.src2trg.pth < INPUT.TXT > OUTPUT.TXT

For more details and additional options, run the above scripts with the --help flag.

Copyright (C) 2018, Mikel Artetxe

Licensed under the terms of the GNU General Public License, either version 3 or (at your option) any later version. A full copy of the license can be found in LICENSE.txt.