基于语义语法分析的中文语句困惑度评价

Evaluating perplexity of Chinese sentences based on grammar & semantics analysis

目前用来评价机器翻译系统译文质量的方法主要有IBM提出的BLEU、TER和METEOR等方法,分别以词汇的重现率、译文与参考译文之间的编辑距离和语言学知识等特征作为评价依据,在判定中文句子的困惑度方面具有一定局限性。提出在依存语法分析的基础上,通过对中文句子及其句子主干的语法和语义两方面进行分析得出中文句子的困惑度。实验证明这种方法比通过译文加权改进后的BLEU方法准确率高出4%。

Methods currently used to evaluate the translation quality of machine translation system are BLEU introduced by IBM,TER,METEOR. These methods do evaluation according to the recurrence rate,the edit distance and the linguistic knowledge,respectively. Although they are superior to other methods,they have some defects in judging the perplexity of Chinese sentences. This paper proposed a method for evaluating the perplexity of Chinese sentences. Specifically,it analyzed the grammar and the semantics together for Chinese sentences and their trunks,based on the parsing of dependency grammar. In the comparison experiments,this method outperforms the mainstream evaluating ones,with accuracy 4 percent higher than improved weighting BLEU method.