Applying Morphology Generation Models to Machine Translation

  • Kristina Toutanova
  • Hisami Suzuki
  • Achim Ruopp

Proceedings of ACL |

Published by Association for Computational Linguistics

View Publication

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.