北京大学学报（自然科学版）2026，Vol.62Issue(5)：977-985,9.DOI:10.13209/j.0479-8023.2026.007
融合翻译难度的机器译文自动评价
Automatic Evaluation of Machine Translation Incorporating Translation Difficulty
摘要
Abstract
Mainstream automatic evaluation metrics of machine translation typically treat all input sentences equally,overlooking the variation in translation difficulty posed by different source sentences.This often leads to inadequate reflection of system performance on structurally complex or lexically rare content.To address this limitation,we propose a novel evaluation approach TD_MT that integrates translation difficulty as a core factor in the scoring process.By analyzing representative samples using large language models and extracting syntactic and lexical features,such as dependency tree depth and word frequency,a linguistically interpretable,rule-based framework for translation difficulty classification is constructed.Evaluation scores are dynamically adjusted based on the estimated difficulty levels of source sentences,enabling a more nuanced assessment.At the sentence level,the proposed difficulty-aware machine translation evaluation metric exceeds the best-performing participating metric,XCOMET-Ensemble,by 0.004.At the system level,it exceeds XCOMET-Ensemble by 0.060.Experimental results on the WMT'23 benchmark dataset demonstrate that,compared with classical evaluation metrics and XCOMET-Ensemble,the proposed method achieves a significantly higher correlation with human judgments.关键词
机器翻译/机器译文自动评价/翻译难度/大语言模型Key words
machine translation/automatic evaluation of machine translation/translation difficulty/large language model引用本文复制引用
唐铭,李茂西..融合翻译难度的机器译文自动评价[J].北京大学学报（自然科学版）,2026,62(5):977-985,9.基金项目
国家自然科学基金(62366020,61662031)和江西省教育厅研究生创新基金(YC2024-S271)资助 （62366020,61662031）
