医学文本的机器翻译质量:基于标准化评估指标的分析
Pascal Block1, Johanna Schaefer1, Felix Maurer1
1Goethe University Frankfurt, University Medicine, Institute of Medical Informatics (IMI), Frankfurt am Main, Germany.
Studies in health technology and informatics
|September 3, 2025
概括
常见的机器翻译系统为罕见病医学文本提供了可接受的质量,而英语翻译的表现最好. METEOR和COMET指标显示质量中等至高,尽管BLEU得分较低.
科学领域:
- 医疗信息学
- 自然语言处理
背景情况:
- 罕见病患者的护理是欧盟的一个跨境问题,SE-ATLAS的使用反映了语言的多样性.
- 尽管SE-ATLAS网站提供德国的罕见病信息,但其仅提供德语的内容给非德语人士带来了语言障碍.
研究的目的:
- 评估医学文本的常用机器翻译系统的质量.
- 确定机器翻译是否能够克服语言障碍,从而获取罕见疾病信息.
主要方法:
- 分析DeepL,ChatGPT和谷歌翻译用于德语到英语,法语和波兰语的医学文本翻译.
- 使用标准化指标进行客观质量评估:BLEU,METEOR和COMET,与参考翻译进行比较.
主要成果:
- 在METEOR和COMET指标中,翻译质量中等至高,而在BLEU指标中,翻译质量较低.
- 与法语和波兰语翻译相比,英语翻译的质量通常更高.
结论:
- 在这三种系统中没有发现翻译质量的统计学上显著差异,所有系统都产生了可接受的结果.
- 除了翻译质量之外的因素,如数据保护,成本和整合,应指导系统的选择.
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