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This codebase is forked from facebook's MUSE which implements Word Translation without Parallel Data.

Additions to the forked codebase implement Earth Mover’s Distance Minimization for Unsupervised Bilingual Lexicon Induction.

• Python 2/3 with numpy/scipy
• PyTorch

Get monolingual and cross-lingual word embeddings evaluation datasets by simply running (in data/): ./get_evaluation.sh

For pre-trained monolingual word embeddings, download fastText Wikipedia embeddings.

You can download the English (en) and Chinese (zh) embeddings this way:

# English fastText Wikipedia embeddings
curl -Lo data/wiki.en.vec https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.en.vec
# Spanish fastText Wikipedia embeddings
curl -Lo data/wiki.zh.vec https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.zh.vec

To train the Conneau et al. (2017) model run:

python unsupervised.py --src_lang en --tgt_lang zh --src_emb data/wiki.en.vec --tgt_emb data/wiki.zh.vec

To train the Conneau et al. (2017) model run:

python unsupervised_wgan.py --src_lang en --tgt_lang zh --src_emb data/wiki.en.vec --tgt_emb data/wiki.zh.vec

By default, the validation metric is the mean cosine of word pairs from a synthetic dictionary built with CSLS (Cross-domain similarity local scaling). Pay close attention to all default flags before running experiments.