Abstract
In this paper, we propose a phrase reordering model for statistical machine translation. The model is derived from the bracketing ITG, and integrates the local and global reordering model. We present a method to extract phrase pairs from a word-aligned bilingual corpus in which the alignments satisfy the ITG constraint, and we also extract the reordering information for the phrase pairs, which are used to build the re-ordering model. Through experiments, we show that this model obtains significant improvements over the baseline on a Chinese-English translation.
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Chao, WH., Li, ZJ., Chen, YX. (2007). An Integrated Reordering Model for Statistical Machine Translation. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_91
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DOI: https://doi.org/10.1007/978-3-540-76631-5_91
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