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 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.
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Corston-Oliver, S., Gamon, M. (2004). Normalizing German and English Inflectional Morphology to Improve Statistical Word Alignment. In: Frederking, R.E., Taylor, K.B. (eds) Machine Translation: From Real Users to Research. AMTA 2004. Lecture Notes in Computer Science(), vol 3265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30194-3_6
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DOI: https://doi.org/10.1007/978-3-540-30194-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23300-8
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