MUSE is a Python library for multilingual word embeddings, whose goal is to provide the community with:
- state-of-the-art multilingual word embeddings based on fastText
- large-scale high-quality bilingual dictionaries for training and evaluation
We include two methods, one supervised that uses a bilingual dictionary or identical character strings, and one unsupervised that does not use any parallel data (see Word Translation without Parallel Data for more details).
- Python 2/3 with NumPy/SciPy
- PyTorch
- Faiss (recommended) for fast nearest neighbor search (CPU or GPU).
MUSE is available on CPU or GPU, in Python 2 or 3. Faiss is optional for GPU users - though Faiss-GPU will greatly speed up nearest neighbor search - and highly recommended for CPU users. Faiss can be installed using "conda install faiss-cpu -c pytorch" or "conda install faiss-gpu -c pytorch".
Get monolingual and cross-lingual word embeddings evaluation datasets:
- Our 110 bilingual dictionaries
- 28 monolingual word similarity tasks for 6 languages, and the English word analogy task
- Cross-lingual word similarity tasks from SemEval2017
- Sentence translation retrieval with Europarl corpora
by simply running (in data/):
./get_evaluation.shNote: Requires bash 4. The download of Europarl is disabled by default (slow), you can enable it here.
For pre-trained monolingual word embeddings, we highly recommend fastText Wikipedia embeddings, or using fastText to train your own word embeddings from your corpus.
You can download the English (en) and Spanish (es) 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.es.vec https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.es.vecThis project includes two ways to obtain cross-lingual word embeddings:
- Supervised: using a train bilingual dictionary (or identical character strings as anchor points), learn a mapping from the source to the target space using (iterative) Procrustes alignment.
- Unsupervised: without any parallel data or anchor point, learn a mapping from the source to the target space using adversarial training and (iterative) Procrustes refinement.
For more details on these approaches, please check here.
To learn a mapping between the source and the target space, simply run:
python supervised.py --src_lang en --tgt_lang es --src_emb data/wiki.en.vec --tgt_emb data/wiki.es.vec --n_iter 5 --dico_train defaultBy default, dico_train will point to our ground-truth dictionaries (downloaded above); when set to "identical_char" it will use identical character strings between source and target languages to form a vocabulary. Logs and embeddings will be saved in the dumped/ directory.
To learn a mapping using adversarial training and iterative Procrustes refinement, run:
python unsupervised.py --src_lang en --tgt_lang es --src_emb data/wiki.en.vec --tgt_emb data/wiki.es.vecBy default, the validation metric is the mean cosine of word pairs from a synthetic dictionary built with CSLS (Cross-domain similarity local scaling).
We also include a simple script to evaluate the quality of monolingual or cross-lingual word embeddings on several tasks:
Monolingual
python evaluate.py --src_lang en --src_emb data/wiki.en.vec --max_vocab 200000Cross-lingual
python evaluate.py --src_lang en --tgt_lang es --src_emb data/wiki.en-es.en.vec --tgt_emb data/wiki.en-es.es.vec --max_vocab 200000We provide multilingual embeddings and ground-truth bilingual dictionaries.
We release fastText Wikipedia supervised word embeddings for 30 languages, aligned in a single vector space.
| Arabic: text | Bulgarian: text | Catalan: text | Croatian: text | Czech: text | Danish: text |
| Dutch: text | English: text | Estonian: text | Finnish: text | French: text | German: text |
| Greek: text | Hebrew: text | Hungarian: text | Indonesian: text | Italian: text | Macedonian: text |
| Norwegian: text | Polish: text | Portuguese: text | Romanian: text | Russian: text | Slovak: text |
| Slovenian: text | Spanish: text | Swedish: text | Turkish: text | Ukrainian: text | Vietnamese: text |
We created 110 large-scale ground-truth bilingual dictionaries using an internal translation tool. The dictionaries handle well the polysemy of words. We provide a train and test split of 5000 and 1500 unique source words, as well as a larger set of up to 100k pairs. Our goal is to ease the development and the evaluation of cross-lingual word embeddings and multilingual NLP.
European languages in every direction
| src-tgt | German | English | Spanish | French | Italian | Portuguese |
|---|---|---|---|---|---|---|
| German | - | full train test | full train test | full train test | full train test | full train test |
| English | full train test | - | full train test | full train test | full train test | full train test |
| Spanish | full train test | full train test | - | full train test | full train test | full train test |
| French | full train test | full train test | full train test | - | full train test | full train test |
| Italian | full train test | full train test | full train test | full train test | - | full train test |
| Portuguese | full train test | full train test | full train test | full train test | full train test | - |
Other languages to English (e.g. {fr,es}-en)
English to other languages (e.g. en-{fr,es})
Please cite [1] if you found the resources in this repository useful.
[1] A. Conneau*, G. Lample*, L. Denoyer, MA. Ranzato, H. Jégou, Word Translation Without Parallel Data
* Equal contribution. Order has been determined with a coin flip.
@article{conneau2017word,
title={Word Translation Without Parallel Data},
author={Conneau, Alexis and Lample, Guillaume and Ranzato, Marc'Aurelio and Denoyer, Ludovic and J{\'e}gou, Herv{\'e}},
journal={arXiv preprint arXiv:1710.04087},
year={2017}
}
MUSE is the project at the origin of the work on unsupervised machine translation with monolingual data only [2].
[2] G. Lample, L. Denoyer, MA. Ranzato Unsupervised Machine Translation With Monolingual Data Only
@article{lample2017unsupervised,
title={Unsupervised Machine Translation Using Monolingual Corpora Only},
author={Lample, Guillaume and Denoyer, Ludovic and Ranzato, Marc'Aurelio},
journal={arXiv preprint arXiv:1711.00043},
year={2017}
}
Contact: gl@fb.com aconneau@fb.com