Normalizing German and english inflectional morphology to improve statistical word alignment

Abstract

German has a richer system of inflectional morphology than English, which causes problems for current approaches to statistical word alignment. Using Giza-H- as a reference implementation of the IBM Model 1, an HMMbased alignment and IBM Model 4, we measure the impact of normalizing inflectional morphology on German-English statistical word alignment. We demonstrate that normalizing inflectional morphology improves the perplexity of models and reduces alignment errors. © Springer-Verlag 2004.