人工智能中国特色话语汉英翻译质量研究
Research on Chinese-English Translation Quality of Discourse with Chinese Characteristics by Artificial Intelligence
摘要: 随着人工智能技术的快速发展，人工智能在中国特色话语中的应用逐渐成为研究热点。本文对比分析了GPT-4o与文心一言3.5在中国特色话语汉英翻译中的表现差异。本文使用了BLEU值、Flesch-Kincaid Grade Level和Gunning Fog Index等工具，以官方译文为标准进行了译文准确度与复杂度对比测试。初步分析表明，文心一言3.5在准确度与复杂度方面比GPT-4o更接近于官方译文。研究还发现，当前人工智能技术尚无法完全替代人工译者在该领域的作用。本文对于人机协作进行中国特色话语翻译和未来人工智能翻译工具优化方向提出了建议。
Abstract: With the rapid development of artificial intelligence technology, the application of artificial intelligence in discourse with Chinese characteristics has gradually become a research hotspot. This paper compared and analyzed the performance difference between GPT-4o and ERNIE Bot 3.5 in Chinese-English translation of discourse with Chinese characteristics. In this paper, tools such as BLEU value, Flesch-Kincaid Grade Level and Gunning Fog Index were used to conduct a comparative test of translation accuracy and complexity using the official translation as the standard. Preliminary analysis shows that ERNIE Bot 3.5 is closer to the official translation than GPT-4o in terms of accuracy and complexity. The study also found that current AI technologies are not yet able to completely replace the role of human translators in this field. This paper makes suggestions for human-computer collaboration for discourse with Chinese characteristics translation and future directions for optimizing AI translation tools.
文章引用：凌颖. 人工智能中国特色话语汉英翻译质量研究[J]. 现代语言学, 2025, 13(4): 575-581. https://doi.org/10.12677/ml.2025.134385

参考文献

[1] 胡开宝, 李晓倩. 大语言模型背景下翻译研究的发展: 问题与前景[J]. 中国翻译, 2023, 44(6): 64-73.
[2] 刘海涛, 亓达. 大语言模型的语用能力探索——从整体评估到反语分析[J]. 现代外语, 2024, 47(4): 439-451.
[3] 王贇, 张政. ChatGPT人工智能翻译的隐忧与纾解[J]. 中国翻译, 2024, 45(2): 95-102.
[4] 文旭, 田亚灵. ChatGPT应用于中国特色话语翻译的有效性研究[J]. 上海翻译, 2024(2): 27-34.
[5] 周兴华, 王传英. 人工智能技术在计算机辅助翻译软件中的应用与评价[J]. 中国翻译, 2020, 41(5): 121-129.
[6] 邱大平. 论政治话语外宣翻译取向的二元统一[J]. 中南大学学报(社会科学版), 2018, 24(6): 205-212.
[7] 雷鹏飞, 张浮凌. 基于机器翻译软件的外宣文本翻译质量评估研究[J]. 未来与发展, 2024, 48(6): 41-48.
[8] 任文, 赵田园. 国家对外翻译传播能力研究: 理论建构与实践应用[J]. 上海翻译, 2023(2): 1-7.
[9] 胡开宝. 国家外宣翻译能力: 构成、现状与未来[J]. 上海翻译, 2023(4): 1-7+95.
[10] House, J. (2014) Translation Quality Assessment: Past and Present. In: House, J., Ed., Translation: A Multidisciplinary Approach, Palgrave Macmillan, 241-264. [Google Scholar] [CrossRef] 
[11] 田朋. 翻译多维质量标准MQM模型介评与启示[J]. 东方翻译, 2020(3): 23-30.
[12] Papineni, K., Roukos, S., Ward, T. and Zhu, W. (2001) BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, 7-12 July 2002, 311-318. [Google Scholar] [CrossRef] 
[13] Kincaid, J.P., Fishburne, R.P., Rogers, R.L. and Chissom, B.S. (1975) Derivation of New Readability Formulas (Automated Readability Index, Fog Count, and Flesch Reading Ease Formula) for Navy Enlisted Personnel. Research Branch Report 8-75, Naval Air Station Memphis.
[14] Gunning, R. (1952) The Technique of Clear Writing. McGraw-Hill.
[15] Callison-Burch, C., Osborne, M. and Koehn, P. (2006) Re-Evaluating the Role of BLEU in Machine Translation Re-search. Proceedings of EACL, Trento, 3-7 April 2006, 249-256.