汉蒙翻译模型中的依存语法与形态信息应用研究

该文提出将源语言句法信息和目标语言形态信息引入汉蒙机器翻译的模型构造中,以降低译文的词形错误率等问题。在源语言端,利用汉语依存句法分析器获取依存树,将依存句法信息以标注形式记在每个词上;在目标语言端,分析并获取蒙古语形态信息;利用LOP思想将源语言依存句法信息和目标语言形态信息引入翻译模型构造中。实验表明,其BLEU评分比传统的短语统计翻译模型有明显提高。该方法通过词、短语、句法三层面信息的结合,实现了汉蒙两种语言语法结构的平衡,特别适合于源语言形态信息贫乏而目标语言形态信息丰富的统计机器翻译系统。

Dependency Informed Chinese-to-Mongolian Translation Model with Morphological Information

We present a dependency informed Chinese-to-Mongolian translation model with morphological information to reduce the error of word form. We get the information by adding per-word dependency information to the source language and morphological information to the target. Then, we construct LOP-Factored translation model. Experimental results demonstrate significant improvements of translation quality achieved in terms of BLEU compared to the baseline phrase-based system. Balancing the grammar structure between source and target, this method is particularly suitable for translating morphological poor into rich languages.