@inproceedings{vo-etal-2024-improving-vietnamese,
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Vietnamese-English Medical Machine Translation
%A Vo, Nhu
%A Nguyen, Dat Quoc
%A Le, Dung D.
%A Piccardi, Massimo
%A Buntine, Wray
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F vo-etal-2024-improving-vietnamese
%X 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.
%U https://aclanthology.org/2024.lrec-main.784
%P 8955-8962
Markdown (Informal)
[Improving Vietnamese-English Medical Machine Translation](https://aclanthology.org/2024.lrec-main.784) (Vo et al., LREC-COLING 2024)
ACL
- Nhu Vo, Dat Quoc Nguyen, Dung D. Le, Massimo Piccardi, and Wray Buntine. 2024. Improving Vietnamese-English Medical Machine Translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8955–8962, Torino, Italia. ELRA and ICCL.