Improving Vietnamese-English Medical Machine Translation
This work addresses a gap in machine translation for the Vietnamese-English medical domain, providing a new dataset to support research, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of Vietnamese-English medical machine translation by constructing MedEV, a new parallel dataset of 360K sentence pairs, and found that fine-tuning the 'vinai-translate' model achieved the best performance in experiments.
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.