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A Ranking-based Approach to Word Reordering for Statistical Machine Translation
2012
Annual Meeting of the Association for Computational Linguistics
Long distance word reordering is a major challenge in statistical machine translation research. Previous work has shown using source syntactic trees is an effective way to tackle this problem between two languages with substantial word order difference. In this work, we further extend this line of exploration and propose a novel but simple approach, which utilizes a ranking model based on word order precedence in the target language to reposition nodes in the syntactic parse tree of a source
dblp:conf/acl/YangLZY12
fatcat:jv2qqn7m6vb4fbz7fnpjisrgda