从语法学角度对翻译质量的定量评估的研究
Bojia He1, Jinquan Wang1, Yizhe Wang2
1College of International Studies, Yangzhou University, Yangzhou, Jiangsu, China.
PloS one
|February 13, 2025
概括
句子级别分析显示,bigram的特征最能预测跨类型的翻译质量. 本研究开发了一种自动翻译质量评估模型,用于解释性,论证性和叙事性文本.
科学领域:
- 语言学的语言学.
- 计算语言学 计算语言学
- 翻译研究 翻译研究
背景情况:
- 词法学为分析语言单元提供了理论基础.
- 自动评估翻译质量需要强大的语言特征提取.
- 性别差异可能会影响翻译质量评估指标.
研究的目的:
- 在不同文本类型的短语层面检查翻译质量.
- 构建和验证自动翻译质量评估 (TQA) 模型.
- 识别预测翻译准确性的关键语言特征.
主要方法:
- 提取了与语言形式 (n-gram匹配程度) 和意义 (标记为n-gram匹配程度的部分语音) 相关的六个变量.
- 对解释性,论证性和叙事性论文进行了分析的句子级特征 (大图,三图,四图).
- 开发和评估使用相关系数 (R,R2) 的TQA模型.
主要成果:
- 与Bigram相关的变量显示出对所有类型的翻译成绩最强的预测能力.
- 大图匹配度显示了与解释性文本翻译分数的最高相关性 (r=.752).
- 开发的TQA模型显示出很好的匹配,解释性文本模型实现了最高的相关性 (R=0.820,R2=0.673).
结论:
- 翻译意义和形式的自动评估在各种类型中都是可行的.
- 句子级别分析,特别是使用大图,对于TQA是有效的.
- 开发的模型为自动化翻译评估提供了理论和实际意义.
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