Machine translation (MT) in the medical domain plays a pivotal role in enhancing healthcare quality and disseminating medical knowledge. Despite advancements in English-Thai MT technology, common MT approaches often underperform in the medical field due to their inability to precisely translate medical terminologies. Our research prioritizes not merely improving translation accuracy but also maintaining medical terminology in English within the translated text through code-switched (CS) translation. We developed a method to produce CS medical translation data, fine-tuned a CS translation model with this data, and evaluated its performance against strong baselines, such as Google Neural Machine Translation (NMT) and GPT-3.5/GPT-4. Our model demonstrated competitive performance in automatic metrics and was highly favored in human preference evaluations. Our evaluation result also shows that medical professionals significantly prefer CS translations that maintain critical English terms accurately, even if it slightly compromises fluency. Our code and test set are publicly available https://github.com/preceptorai-org/NLLB_CS_EM_NLP2024.

本研究针对医疗领域的机器翻译技术，解决了现有系统在医疗术语翻译准确性上的不足。通过提出一种代码切换翻译的新方法，保持关键医学术语的准确性，研究发现医疗专业人士更倾向于接受这种尽管流利度稍有妥协的翻译方式，显示出其在提升医疗翻译质量方面的巨大潜力。

创建医疗领域的英语-泰语代码切换机器翻译