We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to learn the grammatical rules and context dependent changes using a phrase reordering classification framework. We consider a variety of machine learning techniques, including state-of-the-art structured prediction methods. Techniques are compared and evaluated on a Chinese-English corpus, a language pair known for the high reordering characteristics which cannot be adequately captured with current models. In the reordering classification task, the method significantly outperforms the baseline against which it was tested, and further, when integrated as a component of the state-of-the-art machine translation system, MOSES, it achieves improvement in translation results. color="gray">
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机译：我们提出了一种用于统计机器翻译（SMT）的距离短语重排序模型（DPR），其目的是使用短语重排序分类框架来学习语法规则和上下文相关的更改。我们考虑了多种机器学习技术，包括最新的结构化预测方法。在汉英语料库上对技术进行比较和评估，这是一种以高重排序特性而著称的语言对，当前模型无法充分捕获这种语言对。在重新排序分类任务中，该方法的性能明显优于测试时所依据的基准，此外，如果将该方法集成为最新的机器翻译系统MOSES的组成部分，则可以改善翻译结果。 color =“ gray”>
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