  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.
