morphogen: Translation into morphologically rich languages with synthetic phrases

Translation into morphologically rich lan-guages is an important but recalcitrant prob-lem in MT. We present a simple and effec-tive approach that deals with the problem in two phases. First, a discriminative model is learned to predict inflections of target words from rich source-side annotations. Then, this model is used to create additional sentence-specific word- and phrase-level translations that are added to a standard translation model as “synthetic ” phrases. Our approach re-lies on morphological analysis of the target language, but we show that an unsupervised Bayesian model of morphology can success-fully be used in place of a supervised analyzer. We report significant improvements in transla-tion quality when translating from English to Russian, Hebrew and Swahili.