MaMuS (Massively Multilingual Sparse Word Representations)

If you want to use the pretrained multilingual sparse word embeddings that served as the basis for the experiments in the ICLR2020 paper Massively Multilingual Sparse Word Representations you can do it so from the above link.

Colab demo

You can also familiarize yourself with the kind of sparse representations we trained in this Colab notebook

Training your own MaMuS word representations

git clone git@github.com:begab/mamus.git
cd mamus

conda create --name mamus python==3.6.3
source activate mamus  
pip install -r requirements.txt && conda install -c conda-forge python-spams  

wget http://www.inf.u-szeged.hu/~berendg/docs/fasttext_cbow100_{en,fr}.vec.gz

python mamus.py --embedding-mode fasttext_cbow100 --dictionary-fallback --dictionary-file dictionaries/massively_multiling/parallel.fwdxbwd-dict.fr-en.gz --source-embedding fasttext_cbow100_en.vec.gz --target-embedding fasttext_cbow100_fr.vec.gz --out-path en_fr_mamus.vec > mamus.log 2>&1 &

Bibtex for the publication

@inproceedings{  
  berend2020massively,  
  title={Massively Multilingual Sparse Word Representations},  
  author={G{\'a}bor Berend},  
  booktitle={International Conference on Learning Representations},  
  year={2020},  
  url={https://openreview.net/forum?id=HyeYTgrFPB}  
}