让翻译更符合专业需求，用规范提升机器翻译质量
Specification-Aware Machine Translation and Evaluation for Purpose Alignment
- 根据翻译规范设计指导性提示，引导模型输出
- 规范指导的LLM译文在专家评估中优于官方人工译文
- 适合需要精准表达的专业场景，如财报、投资说明
在专业场景中，翻译受沟通目标和客户要求驱动，常以规范形式体现。现有评估框架虽重视规范作用，但在机器翻译研究中多仅隐含处理。基于翻译学理论，本文阐明规范在专业翻译中的必要性，并提出可操作的规范感知式机器翻译与评估方法。以33家上市公司投资者关系文本为实验对象，对比五种翻译类型（含官方人工译文与大模型提示生成结果），通过专家错误分析、用户偏好排序及自动指标验证。结果表明，在人类评估中，受规范指导的大型语言模型译文持续优于官方人工译文，揭示了实际质量感知与预期之间的差距。研究证明，在人工监督下将规范融入机器翻译流程，可实现更契合专业实践的翻译质量提升。
原文摘要 · Abstract (English)
In professional settings, translation is guided by communicative goals and client needs, often formalized as specifications. While existing evaluation frameworks acknowledge the importance of such specifications, these specifications are often treated only implicitly in machine translation (MT) research. Drawing on translation studies, we provide a theoretical rationale for why specifications matter in professional translation, as well as a practical guide to implementing specification-aware MT and evaluation. Building on this foundation, we apply our framework to the translation of investor relations texts from 33 publicly listed companies. In our experiment, we compare five translation types, including official human translations and prompt-based outputs from large language models (LLMs), using expert error analysis, user preference rankings, and an automatic metric. The results show that LLM translations guided by specifications consistently outperformed official human translations in human evaluations, highlighting a gap between perceived and expected quality. These findings demonstrate that integrating specifications into MT workflows, with human oversight, can improve translation quality in ways aligned with professional practice.
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