If you have a few free minutes, try, for fun, filling them with Google Translate. And you need not be multilingual to enjoy it. Start with something straightforward: Enter an English phrase or sentence (idioms bring particular pleasure). Click a language, say, Spanish, and then "translate." Copy and paste the translated results over your original English phrase, reverse both languages (so that, in this example, Spanish is now where you begin and English is where you end), and again click "translate."

While parallel corpora are an indispensable resource for data-driven multilingual natural language processing tasks such as machine translation, they are limited in quantity, quality and coverage. As a result, learning translation models from non-parallel corpora has become increasingly important nowadays, especially for low-resource languages. In this work, we propose a joint model for iteratively learning parallel lexicons and phrases from nonparallel corpora. The model is trained using a Viterbi EM algorithm that alternates between constructing parallel phrases using lexicons and updating lexicons based on the constructed parallel phrases. Experiments on Chinese-English datasets show that our approach learns better parallel lexicons and phrases and improves translation performance significantly.