  Crosslingual word embeddings represent lexical items from different languages
in the same vector space, enabling transfer of NLP tools. However, previous
attempts had expensive resource requirements, difficulty incorporating
monolingual data or were unable to handle polysemy. We address these drawbacks
in our method which takes advantage of a high coverage dictionary in an EM
style training algorithm over monolingual corpora in two languages. Our model
achieves state-of-the-art performance on bilingual lexicon induction task
exceeding models using large bilingual corpora, and competitive results on the
monolingual word similarity and cross-lingual document classification task.
