BERTScore is an effective and robust automatic metric for referencebased machine translation evaluation. In this paper, we incorporate multilingual knowledge graph into BERTScore and propose a metric named KG-BERTScore, which linearly combines the results of BERTScore and bilingual named entity matching for reference-free machine translation evaluation. From the experimental results on WMT19 QE as a metric without references shared tasks, our metric KG-BERTScore gets higher overall correlation with human judgements than the current state-of-the-art metrics for reference-free machine translation evaluation.1 Moreover, the pre-trained multilingual model used by KG-BERTScore and the parameter for linear combination are also studied in this paper.

本文研究了机器翻译自动评估方法，并将多语言知识图谱融入BERTScore之中，提出了一种名为KG-BERTScore的评估指标，通过线性组合BERTScore和双语命名实体匹配的结果来评估无需参考译文的机器翻译质量，并在实验中证明KG-BERTScore相比当前无参考机器翻译评价方法更接近于人工评估结果。同时，本文还探究了KG-BERTScore所用到的预训练多语言模型和线性组合参数。

KG-BERTScore：将知识图谱融入BERTScore用于无参考机器翻译评价