Applying Morphology Generation Models to Machine Translation
We improve the quality of statistical machine translation (SMT) by applying models that predict word forms from their stems using extensive morphological and syntactic information from both the source and target languages. Our inﬂection generation models are trained independently of the SMT system. We investigate different ways of combining the inﬂection prediction component with the SMT system by training the base MT system on fully inﬂected forms or on word stems. We applied our inﬂection generation models in translating English into two morphologically complex languages, Russian and Arabic, and show that our model improves the quality of SMT over both phrasal and syntax-based SMT systems according to BLEU and human judgements.