Improving Vietnamese-English Medical Machine Translation
Nhu Vo, Dat Quoc Nguyen, Dung D. Le, Massimo Piccardi, Wray Buntine
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.
BibTeX
@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"
}