Improving Vietnamese English Medical Machine Translation

Vo, N; Nguyen, DQ; Le, DD; Piccardi, M; Buntine, W

Improving Vietnamese English Medical Machine Translation

Vo, N Nguyen, DQ Le, DD Piccardi, M

Buntine, W

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Publisher:: European Language Resources Association (ELRA)
Publication Type:: Conference Proceeding
Citation:: 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, 2024, pp. 8955-8962
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Vo, N
dc.contributor.author	Nguyen, DQ
dc.contributor.author	Le, DD
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604
dc.contributor.author	Buntine, W
dc.date.accessioned	2025-04-23T12:16:47Z
dc.date.available	2025-04-23T12:16:47Z
dc.date.issued	2024-01-01
dc.identifier.citation	2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, 2024, pp. 8955-8962
dc.identifier.uri	http://hdl.handle.net/10453/187009
dc.description.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 (Nguyen et al., 2022b) for each translation direction. We publicly release our dataset to promote further research.
dc.language	en
dc.publisher	European Language Resources Association (ELRA)
dc.relation.ispartof	2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Improving Vietnamese English Medical Machine Translation
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-23T12:16:46Z
pubs.publication-status	Published

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 (Nguyen et al., 2022b) for each translation direction. We publicly release our dataset to promote further research.

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http://hdl.handle.net/10453/187009