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Normalizing German and English Inflectional Morphology to Improve Statistical Word Alignment

Simon Corston-Oliver and Michael Gamon

Abstract

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 HMM-based 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.

Details

Publication typeInproceedings
URLhttp://www.amta.org
PublisherAssociation for Machine Translation in the Americas
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