Papers by Hung-Phong Tran
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation (2025.emnlp-main)
Copied to clipboard
Khai Le-Duc, Tuyen Tran, Bach Phan Tat, Nguyen Kim Hai Bui, Quan Dang Anh, Hung-Phong Tran, Thanh Thuy Nguyen, Ly Nguyen, Tuan Minh Phan, Thi Thu Phuong Tran, Chris Ngo, Khanh Xuan Nguyen, Thanh Nguyen-Tang
| Challenge: | Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers. |
| Approach: | They present a large-scale ST dataset for the medical domain spanning all translation directions in Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models. |
| Outcome: | The multi-language speech translation (ST) and machine translation (MT) in the medical domain is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains. |
Medical Spoken Named Entity Recognition (2025.naacl-industry)
Copied to clipboard
Khai Le-Duc, David Thulke, Hung-Phong Tran, Long Vo-Dang, Khai-Nguyen Nguyen, Truong-Son Hy, Ralf Schlüter
| Challenge: | Named Entity Recognition (NER) aims to extract named entities from speech and categorise them into types like person, location, organization, etc. |
| Approach: | They present a spoken NER dataset in the medical domain using pre-trained models that are encoder-only and sequence-to-sequence. |
| Outcome: | The dataset is the largest spoken NER dataset in the world regarding the number of entity types, featuring 18 distinct types. |