Bayesian Subtree Alignment Model based on Dependency Trees

要旨

Word sequential alignment models work well for similar language pairs, but they are quite inadequate for distant language pairs. It is difficult to align words or phrases of distant languages with high ac-curacy without structural information of the sentences. In this paper, we pro- pose a Bayesian subtree alignment model that in-corporates dependency relations be- tween subtrees in dependency tree struc- tures on both sides. The dependency re- la-tion model is a kind of tree-based re- or-dering model, and can handle non-local reorderings, which sequential word-based models often cannot handle properly. The model is also capable of handling multi-level structures, making it possible to find many-to-many correspondences automat-ically without any heuristic rules. The size of the structures is controlled by non-parametric Bayesian priors. Experimen-tal alignment results show that our model achieves 3.5 points better alignment error rate for English-Japanese than the word sequential alignment model, thereby ver-ifying that the use of dependency informa-tion is effective for structurally different language pairs. 1

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