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 in\ufb02ection generation models are trained independently of the SMT system. We investigate different ways of combining the in\ufb02ection prediction component with the SMT system by training the base MT system on fully in\ufb02ected forms or on word stems. We applied our in\ufb02ection 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 in\ufb02ection generation models are trained independently of the SMT system. 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