We propose a forward translation model consisting of a set of maximum entropy classifiers: a separate classifier is trained for each (sufficiently frequent) source-side lemma. In this way the estimates of translation
probabilities can be sensitive to a large number of features derived from the source sentence (including non-local features, features making use of sentence syntactic structure, etc.). When integrated into English-to-Czech dependency-based translation scenario implemented in the TectoMT framework, the new translation model significantly outperforms the baseline model (MLE) in terms of BLEU. The performance is further
boosted in a configuration inspired by Hidden Tree Markov Models which combines the maximum entropy translation model with the target-language dependency tree model.
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Creator: Common Account
Created: 10/30/10 1:32 PM
Modifier: Common Account
Modified: 11/12/10 9:23 PM
Content, Design & Functionality: ÚFAL, 2006–2016.
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