A Clustered Global Phrase Reordering Model for Statistical Machine Translation

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 713–720, Sydney, July 2006. c⃝2006 Association for Computational Linguistics A Clustered Global Phrase Reordering Model for Statistical Machine Translation Masaaki Nagata NTT Communication Science Laboratories 2-4 Hikaridai, Seika-cho, Souraku-gun Kyoto, 619-0237 Japan nagata.masaaki@labs.ntt.co.jp, Kuniko Saito NTT Cyber Space Laboratories 1-1 Hikarinooka, Yokoshuka-shi Kanagawa, 239-0847 Japan saito.kuniko@labs.ntt.co.jp Kazuhide Yamamoto, Kazuteru Ohashi � Nagaoka University of Technology 1603-1, Kamitomioka, Nagaoka City Niigata, 940-2188 Japan ykaz@nlp.nagaokaut.ac.jp, ohashi@nlp.nagaokaut.ac.jp Abstract In this paper, we present a novel global re- ordering model that can be incorporated into standard phrase-based statistical ma- chine translation. Unlike previous local reordering models that emphasize the re- ordering of adjacent phrase pairs (Till- mann and Zhang, 2005), our model ex- plicitly models the reordering of long dis- tances by directly estimating the parame- ters from the phrase alignments of bilin- gual training sentences. In principle, the global phrase reordering model is condi- tioned on the source and target phrases that are currently being translated, and the previously translated source and tar- get phrases. To cope with sparseness, we use N-best phrase alignments and bilin- gual phrase clustering, and investigate a variety of combinations of conditioning factors. Through experiments, we show, that the global reordering model signifi- cantly improves the translation accuracy of a standard Japanese-English translation task. 1 Introduction Global reordering is essential to the translation of languages with different word orders. Ideally, a model should allow the reordering of any distance, because if we are to translate from Japanese to En- glish, the verb in the Japanese sentence must be moved from the end of the sentence to the begin- ning just after the subject in the English sentence. � Graduated in March 2006 Standard phrase-based translation systems use a word distance-based reordering model in which non-monotonic phrase alignment is penalized based on the word distance between successively translated source phrases without considering the orientation of the phrase alignment or the identi- ties of the source and target phrases (Koehn et al., 2003; Och and Ney, 2004). (Tillmann and Zhang, 2005) introduced the notion of a block (a pair of source and target phrases that are translations of each other), and proposed the block orientation bigram in which the local reordering of adjacent blocks are expressed as a three-valued orienta- tion, namely Right (monotone), Left (swapped), or Neutral. A block with neutral orientation is sup- posed to be less strongly linked to its predecessor block: thus in their model, the global reordering is not explicitly modeled. In this paper, we present a global reordering model that explicitly models long distance re- ordering1. It predicts four type of reordering patterns, namely MA (monotone adjacent), MG (monotone gap), RA (reverse adjacent), and RG (reverse gap). There are based on the identities of the source and target phrases currently being trans- lated, and the previously translated source and tar- get phrases. The parameters of the reordering model are estimated from the phrase alignments of training bilingual …

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