@inproceedings{vo-etal-2024-improving,
    title = "Improving {V}ietnamese-{E}nglish Medical Machine Translation",
    author = "Vo, Nhu  and
      Nguyen, Dat Quoc  and
      Le, Dung D.  and
      Piccardi, Massimo  and
      Buntine, Wray",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.784",
    pages = "8955--8962",
    abstract = "Machine translation for Vietnamese-English in the medical domain is still an under-explored research area. In this paper, we introduce MedEV{---}a high-quality Vietnamese-English parallel dataset constructed specifically for the medical domain, comprising approximately 360K sentence pairs. We conduct extensive experiments comparing Google Translate, ChatGPT (gpt-3.5-turbo), state-of-the-art Vietnamese-English neural machine translation models and pre-trained bilingual/multilingual sequence-to-sequence models on our new MedEV dataset. Experimental results show that the best performance is achieved by fine-tuning {``}vinai-translate{''} for each translation direction. We publicly release our dataset to promote further research.",
}

Machine translation for Vietnamese-English in the medical domain is still an under-explored research area. In this paper, we introduce MedEV{---}a high-quality Vietnamese-English parallel dataset constructed specifically for the medical domain, comprising approximately 360K sentence pairs. We conduct extensive experiments comparing Google Translate, ChatGPT (gpt-3.5-turbo), state-of-the-art Vietnamese-English neural machine translation models and pre-trained bilingual/multilingual sequence-to-sequence models on our new MedEV dataset. Experimental results show that the best performance is achieved by fine-tuning {``}vinai-translate{''} for each translation direction. We publicly release our dataset to promote further research.
Vo, Nhu Nguyen, Dat Quoc Le, Dung D. Piccardi, Massimo Buntine, Wray
Improving Vietnamese-English Medical Machine Translation