An integrated reordering model for statistical machine translation
2007
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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