Lam D. Chau


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Enriching Biomedical Knowledge for Low-resource Language Through Large-scale Translation
Long Phan | Tai Dang | Hieu Tran | Trieu H. Trinh | Vy Phan | Lam D. Chau | Minh-Thang Luong
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Biomedical data and benchmarks are highly valuable yet very limited in low-resource languages other than English, such as Vietnamese. In this paper, we use a state-of-the-art translation model in English-Vietnamese to translate and produce both pretrained and supervised data in the biomedical domains. Thanks to such large-scale translation, we introduce ViPubmedT5, a pretrained Encoder-Decoder Transformer model trained on 20 million translated abstracts from the high-quality public PubMed corpus. ViPubMedT5 demonstrates state-of-the-art results on two different biomedical benchmarks in summarization and acronym disambiguation. Further, we release ViMedNLI - a new NLP task in Vietnamese translated from MedNLI using the recently public En-vi translation model and carefully refined by human experts, with evaluations of existing methods against ViPubmedT5.