Papers by Bach Phan Tat
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder (2025.acl-industry)
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Khai Le-Duc, Phuc Phan, Tan-Hanh Pham, Bach Phan Tat, Minh-Huong Ngo, Thanh Nguyen-Tang, Truong-Son Hy
| Challenge: | Multilingual automatic speech recognition (ASR) in the medical domain is a critical foundational task, serving a wide range of downstream applications such as speech translation, spoken language understanding, and voice-activated assistants. |
| Approach: | They present the first multilingual medical ASR dataset and the first collection of small-to-large end-to end medical APR models spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese. |
| Outcome: | The proposed model covers Vietnamese, English, German, French, and Mandarin Chinese, and is the first multilingual ASR dataset across five languages. |
Sentiment Reasoning for Healthcare (2025.acl-industry)
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| Challenge: | Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both the sentiment label and generates the rationale behind it based on the input transcript. |
| Approach: | They propose a task - Sentiment Reasoning - for both speech and textmodalities and propose 'multimodal multitask framework' . they propose to use a model that generates the rationale behind each predicted label and provides a rationale for model prediction with quality semantically comparable to humans. |
| Outcome: | The proposed task improves model transparency by providing rationale for model prediction with quality semantically comparable to humans while improving model’s classification performance. |
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation (2025.emnlp-main)
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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. |