Machine Transliteration is an important approach for Name-Entity translation.In English to Chinese transliteration,the translation granularityis of great importance.In this paper we introduce a Multi-granularitymachine transliteration method.We use word lattice to combine multiple syllable segmentation,and decode with hierarchical phrase-based translation model.Experimental results show that our method combines the advantage of different granularityand improve the robustness of the system.We achieve an improvement of 3.1 ％ on precision,and 2.2 points on BLEU over the baseline system.%音译是解决人名翻译的重要方法.在英汉人名音译问题中,翻译粒度问题一直是研究的重点之一.该文提出一种基于多粒度的英汉人名音译方法.将多种粒度的英文切分通过词图进行融合,并使用层次短语模型进行解码,从而缓解了由于切分错误而导致的音译错误,提高了系统的鲁棒性.实验结果表明基于多粒度的音译方法融合了基于各种粒度音译方法的优点,在准确率上提高了3.1％,在BLEU取得了2.2个点的显著提升.
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