使用剩余分析来提高机器翻译的错误率准确度
Ľubomír Benko1, Dasa Munkova2, Michal Munk2,3
1Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 01, Nitra, Slovakia. lbenko@ukf.sk.
Scientific reports
|April 23, 2024
概括
本研究使用错误率和残余值来比较统计和神经机器翻译 (MT) 质量. 剩余分析提供了更客观的MT质量评估,为每个MT类型揭示了不同的错误模式.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 机器翻译 (MT) 质量评估对于有效的跨语言沟通至关重要.
- 现有的自动指标可能无法完全捕捉翻译错误的细微差别.
- 比较统计MT和神经MT需要强大的评估方法.
研究的目的:
- 为了比较统计和神经机器翻译系统的性能.
- 引入和评估残留分析作为MT质量评估的新型指标.
- 提供关于统计和神经MT系统不同特定错误类型的见解.
主要方法:
- 使用统计和神经方法评估四个在线MT系统 (谷歌翻译,mt@ec,eTranslation).
- 应用自动MT指标,重点关注错误率和残余分析.
- 对英语和德语到斯洛伐克语翻译的MT输出进行比较分析.
主要成果:
- 与统计MT相比,神经MT在预测和语法语义相关性方面的错误率通常较低.
- 统计MT在词汇语义上的错误率高于神经MT.
- 剩余分析与基于参考的评估相结合,提供了更客观的MT质量衡量标准.
结论:
- 仅仅依赖参考翻译对于全面的MT质量评估是不够的.
- 剩余分析提供了一种有价值的客观方法来比较统计和神经MT输出.
- 了解系统特定的错误模式是提高机器翻译准确性的关键.
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