评价维吾尔文学翻译:使用ChatGPT,谷歌翻译和Bing翻译器进行比较研究
1School of Languages, Literacies and Translation, Universiti Sains Malaysia, Penang, Malaysia; Wuhan University of Engineering Science, Wuhan, China.
PloS one
|October 23, 2025
概括
与神经机器翻译 (NMT) 相比,像ChatGPT这样的生成人工智能 (GenAI) 系统在翻译维吾尔文学文本方面表现出更高的性能. 快速工程显著提高了GenAI的性能.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
- 人工智能在翻译研究中的应用
背景情况:
- 文化丰富的文学文本的翻译给自动化系统带来了独特的挑战.
- 生成型人工智能 (GenAI) 和神经机器翻译 (NMT) 代表了机器翻译的不同方法.
- 评估这些系统对低资源语言和文学类型的有效性至关重要.
研究的目的:
- 为了比较GenAI (ChatGPT) 和NMT系统 (谷歌翻译,Bing翻译) 的翻译质量,将维吾尔文学文本翻译成英语.
- 评估不同提示策略对GenAI翻译性能的影响.
- 用自动化指标和专家判断来评估翻译的准确性,流性和文化适应性.
主要方法:
- 使用ChatGPT,谷歌翻译和Bing翻译器将维吾尔文学的文字 (قۇتادغۇ بىلىك) 翻译成英语.
- 为GenAI系统应用两个不同的提示策略.
- 通过自动指标 (BLEU,ROUGE,METEOR,BERT相似性),自动错误分析 (语法,拼写,风格) 和专家人类评估进行质量评估.
主要成果:
- 聊天GPT,特别是简短的提示,在语义准确性,流性和文化适应性方面超过了NMT系统.
- 宾翻译显示出最高的错误率,特别是拼写错误;谷歌翻译提供了稳定但适度的性能.
- 经过专家分析和案例研究,NMT系统经常通过极性逆转和语义转移来扭曲意义.
结论:
- 当适当提示时,GenAI系统在文学翻译方面比传统的NMT提供了显著的优势.
- 提示工程是优化GenAI用于细微的翻译任务的关键因素.
- 虽然GenAI在适应性方面表现出色,但NMT系统提供了基线稳定性;未来的研究应该探索混合方法和后编辑.
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