中文摘要：

上下文信息对于统计机器翻译（Statistical Machine Translation,SMT）中的规则选择是很重要的,但是之前的SMT模型只利用了句子内部的上下文信息,没有利用到整个篇章的上下文信息。该文提出了一种利用篇章上下文信息的方法来提高规则选择的准确性,从而提高翻译的质量。首先利用向量空间模型获得训练语料的文档和测试集中文档的相似度,然后把相似度作为一个新的特征加入到短语模型中。实验结果表明,在英语到汉语的翻译工作中,该方法可以显著提高翻译质量。在NIST-08和CWMT-08两个测试集上BLEU值都有显著的提高。

英文摘要：

The present statistical machine translation（SMT） models only exploit the context information in a sentence and neglect that in the document which is more useful to find the correct translation.In this paper,we propose a new method of using the context of the whole document to improve the quality of SMT.We obtain the similarities between the documents of the training corpus and the documents of the test set using Vector Space Model.The similarity is then considered as a new feature and integrated into a phrase-based model.Large scale experiments show that our approach improves more than one point for NIST-08 and CWMT-08 in term of BLEU in English to Chinese translation task.