Normalizing German and English Inflectional Morphology to Improve Statistical Word Alignment

German has a richer system of inflectional morphology than English, which causes problems for current approaches to statistical word alignment. Using Giza++ 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.

• Type: Conference
• Year: 2004
• Where: AMTA
• Authors: Simon Corston-Oliver, Michael Gamon
• Keywords: AMTA 2004, IBM Model, Inflectional Morphology, Information Management, Statistical Word Alignment