MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation

• URL: http://arxiv.org/abs/2504.03546v1
• Date: Fri, 04 Apr 2025 15:49:17 GMT
• Title: MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation
• Authors: Khai Le-Duc, Tuyen Tran, Bach Phan Tat, Nguyen Kim Hai Bui, Quan Dang, Hung-Phong Tran, Thanh-Thuy Nguyen, Ly Nguyen, Tuan-Minh Phan, Thi Thu Phuong Tran, Chris Ngo, Nguyen X. Khanh, Thanh Nguyen-Tang,
• Abstract summary: MultiMed-ST is a large-scale ST dataset for the medical domain spanning all translation directions in five languages.<n>With 290,000 samples, our dataset is the largest medical machine translation (MT) dataset.<n>We present the most extensive analysis study in ST research to date, including: empirical baselines, bilingual-multilingual comparative study, end-to-end vs. cascaded comparative study, code-switch analysis, and quantitative-qualitative error analysis.
• Score: 3.6818524036584686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual speech translation (ST) in the medical domain enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we present the first systematic study on medical ST, to our best knowledge, by releasing MultiMed-ST, a large-scale ST dataset for the medical domain, spanning all translation directions in five languages: Vietnamese, English, German, French, Traditional Chinese and Simplified Chinese, together with the models. With 290,000 samples, our dataset is the largest medical machine translation (MT) dataset and the largest many-to-many multilingual ST among all domains. Secondly, we present the most extensive analysis study in ST research to date, including: empirical baselines, bilingual-multilingual comparative study, end-to-end vs. cascaded comparative study, task-specific vs. multi-task sequence-to-sequence (seq2seq) comparative study, code-switch analysis, and quantitative-qualitative error analysis. All code, data, and models are available online: https://github.com/leduckhai/MultiMed-ST.

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