Scholarlink

<p>As Machine Translation (MT) technologies become more advanced, the translation errors they generate are often increasingly subtle. When MT is integrated in ‘Human-in-the-Loop’ (HITL) translation workflows for specialized domains, successful Post-Editing (PE) hinges on the humans involved having in-depth subject competence, as knowledge of the specific terminology and conventions are essential to produce accurate translations. One way of assessing an individual’s expertise is through manual translation tests, a method traditionally used by Language Service Providers (LSPs) and translator educators alike. While manual evaluation can provide the most comprehensive overview of a translator’s abilities, they have the disadvantage of being time-consuming and costly, especially when large numbers of subjects and language pairs are involved. In this work, we report on the experience of creating automated tests with GPT-4 for assessing the ability to recognize domain-specific specialized terminology correspondence in the translation of English-to-Turkish engineering texts in HITL translation workflows. While there may be a level of usefulness in the resulting tests, they are not fit for direct implementation without further refinement.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Creating Terminological Correspondence Recognition Tests with GPT-4: A Case Study in English-to-Turkish Translations in the Engineering Domain

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月10日

查看全文
  • Marina Sánchez-Torrón,
  • Egemen Ipek,
  • Vanessa Enríquez Raído

摘要

As Machine Translation (MT) technologies become more advanced, the translation errors they generate are often increasingly subtle. When MT is integrated in ‘Human-in-the-Loop’ (HITL) translation workflows for specialized domains, successful Post-Editing (PE) hinges on the humans involved having in-depth subject competence, as knowledge of the specific terminology and conventions are essential to produce accurate translations. One way of assessing an individual’s expertise is through manual translation tests, a method traditionally used by Language Service Providers (LSPs) and translator educators alike. While manual evaluation can provide the most comprehensive overview of a translator’s abilities, they have the disadvantage of being time-consuming and costly, especially when large numbers of subjects and language pairs are involved. In this work, we report on the experience of creating automated tests with GPT-4 for assessing the ability to recognize domain-specific specialized terminology correspondence in the translation of English-to-Turkish engineering texts in HITL translation workflows. While there may be a level of usefulness in the resulting tests, they are not fit for direct implementation without further refinement.