Exploiting Similarities among Languages for Machine Translation
Abstract
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.
