@inproceedings{corston-oliver-gamon-2004-normalizing,
title = "Normalizing {G}erman and {E}nglish inflectional morphology to improve statistical word alignment",
author = "Corston-Oliver, Simon and
Gamon, Michael",
editor = "Frederking, Robert E. and
Taylor, Kathryn B.",
booktitle = "Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers",
month = sep # " 28 - " # oct # " 2",
year = "2004",
address = "Washington, USA",
publisher = "Springer",
url = "https://link.springer.com/chapter/10.1007/978-3-540-30194-3_6",
pages = "48--57",
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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<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.</abstract>
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%0 Conference Proceedings
%T Normalizing German and English inflectional morphology to improve statistical word alignment
%A Corston-Oliver, Simon
%A Gamon, Michael
%Y Frederking, Robert E.
%Y Taylor, Kathryn B.
%S Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers
%D 2004
%8 sep 28 oct 2
%I Springer
%C Washington, USA
%F corston-oliver-gamon-2004-normalizing
%X 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.
%U https://link.springer.com/chapter/10.1007/978-3-540-30194-3_6
%P 48-57
Markdown (Informal)
[Normalizing German and English inflectional morphology to improve statistical word alignment](https://link.springer.com/chapter/10.1007/978-3-540-30194-3_6) (Corston-Oliver & Gamon, AMTA 2004)
ACL