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.- Anthology ID:
- 2004.amta-papers.6
- Volume:
- Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers
- Month:
- September 28 - October 2
- Year:
- 2004
- Address:
- Washington, USA
- Editors:
- Robert E. Frederking, Kathryn B. Taylor
- Venue:
- AMTA
- SIG:
- Publisher:
- Springer
- Note:
- Pages:
- 48–57
- Language:
- URL:
- https://link.springer.com/chapter/10.1007/978-3-540-30194-3_6
- DOI:
- Cite (ACL):
- Simon Corston-Oliver and Michael Gamon. 2004. Normalizing German and English inflectional morphology to improve statistical word alignment. In Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 48–57, Washington, USA. Springer.
- Cite (Informal):
- Normalizing German and English inflectional morphology to improve statistical word alignment (Corston-Oliver & Gamon, AMTA 2004)
- PDF:
- https://link.springer.com/chapter/10.1007/978-3-540-30194-3_6