Computational Linguistics

We present a novel approach to word reordering which successfully integrates syntactic structural knowledge with phrase-based SMT. This is done by con-structing a lattice of alternatives based on automatically learned probabilistic syntactic rules. In decoding, the alter-natives are scored based on the output word order, not the order of the input. Unlike previous approaches, this makes it possible to successfully integrate syntactic reordering with phrase-based SMT. On an English-Danish task, we achieve an absolute improvement in translation qual-ity of 1.1 % BLEU. Manual evaluation supports the claim that the present ap-proach is significantly superior to previous approaches.