神经机器翻译在财经文本译后编辑中的应用分析——以《经济学人》为例

An Analytical Study on the Application of Neural Machine Translation in Post-Editing of Financial Texts—Taking The Economist as a Case Example

摘要: 神经机器翻译(NMT)技术显著提升了翻译任务的效率与质量。本文以《经济学人》财经类文章为研究对象，本文选取典型翻译实例，对比分析主流神经机器翻译输出与人工译文在多方面的差异，探讨神经机器翻译在译后编辑实践中的优势与局限。研究发现，NMT在文化传递、情感表达等复杂任务中仍存在不足，尤其在跨领域翻译的上下文理解与语义准确性方面面临挑战。基于案例分析，本文进一步总结了译后编辑中常见的问题类型并讨论领域差异对机器翻译输出的影响，以及人工编辑在修正语义偏差和语用失配中的关键作用。

Abstract: Neural Machine Translation (NMT) technology has significantly improved the efficiency and quality of translation tasks. Focusing on financial and economic articles from The Economist, this paper selects typical translation instances to comparatively analyze the multifaceted differences between mainstream NMT outputs and human translations, exploring the advantages and limitations of NMT in post-editing practice. The study finds that NMT still shows deficiencies in complex tasks such as cultural transfer and affective expression, and faces particular challenges in context understanding and semantic accuracy within cross-domain translation. Based on case analysis, this paper further summarizes common error types encountered in post-editing and discusses the impact of domain differences on machine translation output, as well as the critical role of human editing in correcting semantic deviations and pragmatic mismatches.