Large and diverse language models for statistical machine translation

This paper presents methods to combine large language models trained from diverse text sources and applies them to a state-of-art French–English and Arabic–English ma-chine translation system. We show gains of over 2 BLEU points over a strong baseline by using continuous space language models in re-ranking.