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Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities
Ngoc Quang Luong, Andrei Popescu-Belis, Annette Rios Gonzales, Don Tuggener
We implement a fully probabilistic model to combine the hypotheses of a Spanish anaphora resolution system with those of a Spanish-English machine translation system. The probabilities over antecedents are converted into probabilities for the features of translated pronouns, and are integrated with phrase-based MT using an additional translation model for pronouns. The system improves the translation of several Spanish personal and possessive pronouns into English, by solving translation divergencies such as {`}ella{'} vs. {`}she{'}/{`}it{'} or {`}su{'} vs. {`}his{'}/{`}her{'}/{`}its{'}/{`}their{'}. On a test set with 2,286 pronouns, a baseline system correctly translates 1,055 of them, while ours improves this by 41. Moreover, with oracle antecedents, possessives are translated with an accuracy of 83{\%}.
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