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IBPRIA
2003
Springer

Combining Phrase-Based and Template-Based Alignment Models in Statistical Translation

10 years 4 months ago
Combining Phrase-Based and Template-Based Alignment Models in Statistical Translation
In statistical machine translation, single-word based models have an important deficiency; they do not take contextual information into account for the translation decision. A possible solution called Phrase-Based, consists in translating a sequence of words instead of a single word. We show how this approach obtains interesting results in some corpora. One shortcoming of the phrase-based alignment models is that they do not have the generalization capability in word reordering. A possible solution could be the template-based approach, which uses sequences of classes of words instead of sequences of words. We present a template-based alignment model that uses a Part Of Speech tagger for word classes. We also propose an improved model that combines both models. The basic idea is that if a sequence of words has been seen in training, the phrase-based model can be used; otherwise, the template-based model can be used. We present the results from different tasks.
Jesús Tomás, Francisco Casacuberta
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where IBPRIA
Authors Jesús Tomás, Francisco Casacuberta
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