We present a novel approach to word re-ordering which successfully integrates syn-tactic structural knowledge with phrase-based SMT. This is done by constructing a lattice of alternatives based on automatically learned probabilistic syntactic rules. In decoding, the alternatives are scored based on the output word order, not the order of the input. Un-like previous approaches, this makes it possi-ble to successfully integrate syntactic reorder-ing with phrase-based SMT. On an English-Danish task, we achieve an absolute improve-ment in translation quality of 1.1 % BLEU. Manual evaluation supports the claim that the present approach is significantly superior to previous approaches.
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