基于加权词汇衔接的文档级机器翻译自动评价

Document-Level Automatic Machine Translation Evaluation Based on Weighted Lexical Cohesion

在文档词汇衔接评价LC方法的基础上,提出基于权重的LC,即WLC,该方法通过在文档词图上运行PageRank算法获得词汇权重。根据词性信息使得PageRank算法偏向特定的词汇,并提出PWLC方法。实验表明,在文档级别上,所提出的两种方法与人工评价的相关度都优于LC;融合两种方法后,BLEU和TER在文档级别上的评价性能有显著提高。

Based on LC method, weighted LC （WLC） method is proposed, which assigns weights for words by PageRank algorithm running on word graph of documents. Furthermore, a new method named PWLC is also proposed, which biases PageRank algorithm to words with specific POS tags. The experiment results show that WLC and PWLC have higher Spearman correlation than LC at document-level evaluation. Combined with others metrics, such as BLEU and TER, the proposed metrics both show better performance of evaluation at document level.