  Word translation is a problem in machine translation that seeks to build
models that recover word level correspondence between languages. Recent
approaches to this problem have shown that word translation models can learned
with very small seeding dictionaries, and even without any starting
supervision. In this paper we propose a method to jointly find translations
between a pair of languages. Not only does our method learn translations in
both directions but it improves accuracy of those translations over past
methods.
