Word embeddings are one of the most widely used resources in NLP, as they have proven to be of enormous importance for modeling linguistic phenomena in both supervised and unsupervised settings.
the representation of words in cross-lingual vector spaces (henceforth, crosslingual word embeddings) is quickly gaining in popularity.