@inproceedings{pengpun-etal-2024-creating,
    title = "On Creating an {E}nglish-{T}hai Code-switched Machine Translation in Medical Domain",
    author = "Pengpun, Parinthapat  and
      Tiankanon, Krittamate  and
      Chinkamol, Amrest  and
      Kinchagawat, Jiramet  and
      Chairuengjitjaras, Pitchaya  and
      Supholkhan, Pasit  and
      Aussavavirojekul, Pubordee  and
      Boonnag, Chiraphat  and
      Veerakanjana, Kanyakorn  and
      Phimsiri, Hirunkul  and
      Sae-jia, Boonthicha  and
      Sataudom, Nattawach  and
      Ittichaiwong, Piyalitt  and
      Limkonchotiwat, Peerat",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-emnlp.351/",
    doi = "10.18653/v1/2024.findings-emnlp.351",
    pages = "6055--6073",
    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."
}