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.

介绍了 MedEV 数据集，通过对其进行广泛实验，发现对于每个翻译方向，通过对 'vinai-translate' 进行微调，可以实现最佳性能，进而推动进一步的研究。

改善越南 - 英文医学机器翻译

Improving Vietnamese-English Medical Machine Translation

We introduce a high-quality and large-scale Vietnamese-English parallel
dataset of 3.02M sentence pairs, which is 2.9M pairs larger than the benchmark
Vietnamese-English machine translation corpus IWSLT15. We conduct experiments
comparing strong neural baselines and well-known automatic translation engines
on our dataset and find that in both automatic and human evaluations: the best
performance is obtained by fine-tuning the pre-trained sequence-to-sequence
denoising auto-encoder mBART. To our best knowledge, this is the first
large-scale Vietnamese-English machine translation study. We hope our publicly
available dataset and study can serve as a starting point for future research
and applications on Vietnamese-English machine translation.

本文介绍了一个高质量、大规模的越南语 - 英语平行数据集，进行了机器翻译的比较实验，结果表明在预训练序列到序列去噪自编码器 mBART 上的微调获得了最好的性能，这是第一个大规模的越南语 - 英语机器翻译研究，希望这个公开的数据集和研究能成为未来越南语 - 英语机器翻译研究和应用的起点。