利用句法短语改善统计机器翻译性能

An Improved Syntactic Phrase Extraction Approach for Statistical Machine Translation

短语表是基于短语的统计机器翻译系统的一个核心组成部分,基于启发式方法抽取到的短语表受单词对齐错误和未对齐词的影响严重,同时抽取到的短语也并非句法意义上的短语。该文提出一种基于EM(Expectation-maximization)算法的双语句法短语抽取方法来抽取双语句法短语,此方法可以通过不断迭代的方式使各参数值达到最优。通过加入双语句法短语、增加新特征、重新训练三种不同的方法,将获得的双语句法短语与基于短语的统计机器翻译方法结合以提高统计机器翻译系统的性能。结果表明:三种方法都不同程度提高了译文的BLEU(BiLingual Evaluation Understudy)值,其中增加新特征方法提高了0.64个点。

The phrase table lies at the core of a phrase-based statistical machine translation system. The extracted phrase table based on heuristic methods is affected by incorrect word alignments, the unaligned words, and the absence of syntactic information. This paper presents a bilingual syntactic phrases extraction method based on the Expectation-maximization algorithm,which can optimize all parameters by iteratiions. Three techniques are examined to integrate bilingual syntactic phrases to the phrase-based machine translation System： direct augmentation of bilingual phrass,adding new features and re-training. Experiments show that all the three methods improve the BLEU score to varying degrees,with the top increase of 0.64 BLEU score by adding new features.