  Dictionaries and phrase tables are the basis of modern statistical machine
translation systems. This paper develops a method that can automate the process
of generating and extending dictionaries and phrase tables. Our method can
translate missing word and phrase entries by learning language structures based
on large monolingual data and mapping between languages from small bilingual
data. It uses distributed representation of words and learns a linear mapping
between vector spaces of languages. Despite its simplicity, our method is
surprisingly effective: we can achieve almost 90% precision@5 for translation
of words between English and Spanish. This method makes little assumption about
the languages, so it can be used to extend and refine dictionaries and
translation tables for any language pairs.
