Statistical Phrase Alignment Model Using Dependency Relation Probability

Abstract

When aligning very different language pairs, the most important needs are the use of structural information and the capability of generating one-to-many or many-to-many correspondences. In this paper, we propose a novel phrase alignment method which models word or phrase dependency relations in dependency tree structures of source and target languages. The dependency relation model is a kind of tree-based reordering model, and can handle non-local reorderings which sequential word-based models often cannot handle properly. The model is also capable of estimating phrase correspondences automatically without any heuristic rules. Experimental results of alignment show that our model could achieve F-measure 8.5 points higher than the conventional word alignment model with symmetrization algorithms.