A Novel Word Reordering Method for Statistical Machine Translation

Word order differences between source and target languages pose a serious challenge to statistical machine translation (SMT). Pre-ordering, an approach that reorders source words into a target-word-like order as a preprocessing step, has been shown effective in handling word order between different languages and improving translation performance of SMT. In this paper, we propose a novel word reordering method based on the pre-ordering framework. Instead of using a supervised parser trained on a monolingual treebank, our method extracts bilingual structural information for reordering from automatically word-aligned sentence pairs into dependency-tree-like structures, then learns a reordering model by training a dependency parser on this extracted pseudo-treebank. Experiment results show that our pre-ordering method is effective in permuting source words to resemble word order of the target language, and improving translation quality.