Translation into morphologically rich languages is an important but recalcitrant problem in MT. We present a simple and effective 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 sentencespecific word-and phrase-level translations that are added to a standard translation model as "synthetic" phrases. Our approach relies on morphological analysis of the target language, but we show that an unsupervised Bayesian model of morphology can successfully be used in place of a supervised analyzer. We report significant improvements in translation quality when translating from English to Russian, Hebrew and Swahili.
Chahuneau et al. REF have proposed a method for translating English into morphologically rich languages by building a model for predicting inflections of words in the target language.
Translating into Morphologically Rich Languages with Synthetic Phrases
EMNLP
Computer Science