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2006

Semi-Supervised Training for Statistical Word Alignment

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Semi-Supervised Training for Statistical Word Alignment
We introduce a semi-supervised approach to training for statistical machine translation that alternates the traditional Expectation Maximization step that is applied on a large training corpus with a discriminative step aimed at increasing word-alignment quality on a small, manually word-aligned sub-corpus. We show that our algorithm leads not only to improved alignments but also to machine translation outputs of higher quality.
Alexander Fraser, Daniel Marcu
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Alexander Fraser, Daniel Marcu
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