该模型的实验结果比经典的短语统计翻译模型的BLEU评分有明显提高。

Experimental results show that the BLEU scores of the translation results were significantly improved compared with a conventional statistical phrase-based model.

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本文详细比较了两种机器学习方法和两种统计翻译模型在英汉人名音译上的应用效果。

This paper applies to the problem of English-Chinese Name Transliteration both the machine learning and the mac.

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为了处理统计机器翻译中的长距离调序，在基于短语的统计翻译模型的基础上提出了句法调序模型。

To deal with the long-distance reordering, a linguistically syntax-based reordering model was presented for phrasal statistical machine translation.

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