On Creating an English-Thai Code-switched Machine Translation in Medical Domain

TL;DR

This paper introduces a code-switched translation approach for English-Thai medical texts, emphasizing the preservation of medical terminology, and demonstrates its effectiveness through automatic metrics and human evaluations.

Contribution

The study presents a novel method to generate code-switched medical translation data and fine-tune translation models to better preserve critical English medical terms in Thai translations.

Findings

CS translation model outperforms baseline models in preserving medical terms

Medical professionals prefer CS translations with accurate English terminology

Model achieves competitive automatic evaluation scores

Abstract

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.