Massively Multilingual Word Embeddings

Waleed Ammar; George Mulcaire; Yulia Tsvetkov; Guillaume Lample; Chris Dyer; Noah A. Smith

Paper

Massively Multilingual Word Embeddings

Abstract

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 in this area, along with open-source releases of all our methods.