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NAACL
2007

Source-Language Features and Maximum Correlation Training for Machine Translation Evaluation

11 years 2 months ago
Source-Language Features and Maximum Correlation Training for Machine Translation Evaluation
We propose three new features for MT evaluation: source-sentence constrained n-gram precision, source-sentence reordering metrics, and discriminative unigram precision, as well as a method of learning linear feature weights to directly maximize correlation with human judgments. By aligning both the hypothesis and the reference with the sourcelanguage sentence, we achieve better correlation with human judgments than previously proposed metrics. We further improve performance by combining individual evaluation metrics using maximum correlation training, which is shown to be better than the classification-based framework.
Ding Liu, Daniel Gildea
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NAACL
Authors Ding Liu, Daniel Gildea
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