Bayesian Subtree Alignment Model based on Dependency Trees

Proceedings of the 5th International Joint Conference on Natural Language Processing, pages 794–802, Chiang Mai, Thailand, November 8 – 13, 2011. c⃝2011 AFNLP Bayesian Subtree Alignment Model based on Dependency Trees Toshiaki Nakazawa Sadao Kurohashi Graduate School of Informatics, Kyoto University Yoshida-honmachi, Sakyo-ku Kyoto, 606-8501, Japan nakazawa@nlp.kuee.kyoto-u.ac.jp kuro@i.kyoto-u.ac.jp Abstract 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 Introduction Alignment accuracy is crucial for providing high quality corpus-based machine translation systems because translation knowledge is acquired from an aligned training corpus. For similar language pairs, alignment accuracy is high, and the state-of- the-art word alignment tool GIZA++ has a smaller than 10% alignment error rate (AER) for French- English. GIZA++ is an implementation of the alignment models called the IBM models (Brown et al., 1993), which handle sentences as sequences of words, usually followed by some heuristic sym- metrization rules to combine the alignment results in both directions. Since the accuracy is to some extent good for some language pairs, many re- searchers focus not on alignment, but on transla- tion with more linguistic information incorporated in the models. Phrase-based SMT (Koehn et al., 2003) uses units larger than words, whereas Hi- ero (Chiang, 2005) used a kind of sentence struc- ture. Various other studies tried incorporating additional linguistic knowledge such as syntactic trees (Chiang, 2010), dependency trees (Menezes and Quirk, 2008), or packed-forests (Tu et al., 2010) in their translation models. Note that all these works are based on the word sequential alignment models. However, for distant language pairs such as English-Japanese or Chinese-Japanese, the word sequential model is quite inadequate (about 20 to 30 % AER), and therefore it is important to im- prove the alignment accuracy itself. The differ- ences between languages can be seen in Figure 1, which shows an example of English-Japanese. The word or phrase order is quite different for these languages. Another important point is that there are often many-to-one or many-to-many cor- respondences. For example, the Japanese noun phrase “受 光 素子” is composed of three words, whereas the corresponding …

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