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.
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