An exploration of post-translation editing issues on translating transportation science and technology texts using ChatGPT - based on text structure
ChatGPT翻译交通科技文本的译后编辑探究——基于篇章结构

摘要

本文基于交通科技文本实例，从篇章结构的词汇衔接与结构衔接两个方面分析了聊天机器人ChatGPT在汉英翻译任务中的表现，并重点关注在此基础上的译后编辑过程。文章选用图书《国外铁路长大货物运输》部分内容，要求ChatGPT基于生成的优秀提示语指令完成翻译任务，随后笔者在此基础上进行译后编辑，并总结ChatGPT在交通科技文本汉英翻译领域中的表现情况以及译员在译后编辑中注意事项，供译者在翻译实践中参考和借鉴。结果发现：其在句法衔接方面表现优异，词汇与语义方面表现欠佳，仍需大量译后编辑工作。因此，译员在接纳和引导翻译技术发展的同时，也要提高自身能力，因为人工参与是一个关键因素，在数量和质量上都不可或缺，它促进了人与机器之间相互依存和共生的局面。

Abstract

This paper based on examples from transportation scientific and technology texts, analyzes the performance of the chatbot ChatGPT in Chinese-English translation tasks from two aspects: lexical cohesion and structural cohesion within the discourse structure, with a particular focus on the post-editing process that follows. The paper selects portions of the book Railway Transport of Long and Large Goods in Foreign Countries and tasks ChatGPT to complete the translation based on well-crafted prompt instructions. Subsequently, the author conducts post-editing on this basis and summarizes the performance of ChatGPT in the field of Chinese-English translation of transport science and technology texts, as well as the considerations for translators during post-editing, providing a reference for translators in practice. The results reveal that ChatGPT excels in syntactic cohesion but performs poorly in vocabulary and semantics, necessitating substantial post-editing work. Therefore, while translators embrace and guide the development of translation technology, they must also enhance their own skills, as human involvement is an indispensable and crucial component, both in quantity and quality, thereby promoting the ideal of interdependence and symbiosis between humans and machines into reality.