Corpus ID: 1227830

Massively Multilingual Word Embeddings

@article{Ammar2016MassivelyMW,
  title={Massively Multilingual Word Embeddings},
  author={Waleed Ammar and George Mulcaire and Yulia Tsvetkov and Guillaume Lample and Chris Dyer and Noah A. Smith},
  journal={ArXiv},
  year={2016},
  volume={abs/1602.01925}
}

Waleed Ammar, George Mulcaire, +3 authors Noah A. Smith

Published 2016

Computer Science

ArXiv

We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do not require parallel data. Our new evaluation method, multiQVEC-CCA, is shown to correlate better than previous ones with two downstream tasks (text categorization and parsing). We also describe a web portal for evaluation that will facilitate further research… CONTINUE READING

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