Paper: Applying Morphology Generation Models to Machine Translation

ACL ID P08-1059
Title Applying Morphology Generation Models to Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

We improve the quality of statistical machine translation (SMT) by applying models that predict word forms from their stems using extensive morphological and syntactic infor- mation from both the source and target lan- guages. Our inflection generation models are trained independently of the SMT system. We investigate different ways of combining the in- flection 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 judge- ments.