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2008

Applying Morphology Generation Models to Machine Translation

11 years 1 months ago
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.
Kristina Toutanova, Hisami Suzuki, Achim Ruopp
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ACL
Authors Kristina Toutanova, Hisami Suzuki, Achim Ruopp
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