  Learning word embeddings using distributional information is a task that has
been studied by many researchers, and a lot of studies are reported in the
literature. On the contrary, less studies were done for the case of multiple
languages. The idea is to focus on a single representation for a pair of
languages such that semantically similar words are closer to one another in the
induced representation irrespective of the language. In this way, when data are
missing for a particular language, classifiers from another language can be
used.
