tailieunhanh - Báo cáo khoa học: "A Clustered Global Phrase Reordering Model for Statistical Machine Translation"

In this paper, we present a novel global reordering model that can be incorporated into standard phrase-based statistical machine translation. Unlike previous local reordering models that emphasize the reordering of adjacent phrase pairs (Tillmann and Zhang, 2005), our model explicitly models the reordering of long distances by directly estimating the parameters from the phrase alignments of bilingual training sentences. In principle, the global phrase reordering model is conditioned on the source and target phrases that are currently being translated, and the previously translated source and target phrases. . | A Clustered Global Phrase Reordering Model for Statistical Machine Translation Masaaki Nagata Kuniko Saito NTT Communication Science Laboratories NTT Cyber Space Laboratories 2-4 Hikaridai Seika-cho Souraku-gun 1-1 Hikarinooka Yokoshuka-shi Kyoto 619-0237 Japan Kanagawa 239-0847 Japan Kazuhide Yamamoto Kazuteru Ohashi Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka City Niigata 940-2188 Japan ykaz@ ohashi@ Abstract In this paper we present a novel global reordering model that can be incorporated into standard phrase-based statistical machine translation. Unlike previous local reordering models that emphasize the reordering of adjacent phrase pairs Tillmann and Zhang 2005 our model explicitly models the reordering of long distances by directly estimating the parameters from the phrase alignments of bilingual training sentences. In principle the global phrase reordering model is conditioned on the source and target phrases that are currently being translated and the previously translated source and target phrases. To cope with sparseness we use N-best phrase alignments and bilingual phrase clustering and investigate a variety of combinations of conditioning factors. Through experiments we show that the global reordering model significantly 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 English the verb in the Japanese sentence must be moved from the end of the sentence to the beginning 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 .