Clasificación y generalización de formas verbales en sistemas de traducción estocástica

This paper introduces a method to incorporate linguistic knowledge regarding verb forms into an stochastic machine translation model. By means of a rule-based classification of these forms, and by substituting them by the base form of the head verb during the training stage, we achieve a better statistical word alignment, which leads to a better estimate of the translation model. Furthermore, a successful generalization strategy can be devised to produce a new translation for unseen verb forms from the translations of seen verb forms. An evaluation of this method in an English to Spanish limited-domain translation task is presented, producing a significant performance improvement.