Enriching Biomedical Knowledge for Low-resource Language Through Translation

• URL: http://arxiv.org/abs/2210.05598v1
• Date: Tue, 11 Oct 2022 16:35:10 GMT
• Title: Enriching Biomedical Knowledge for Low-resource Language Through Translation
• Authors: Long Phan, Tai Dang, Hieu Tran, Vy Phan, Lam D. Chau, and Trieu H. Trinh
• Abstract summary: We make use of a state-of-the-art translation model in English-Vietnamese to translate and produce both pretrained as well as supervised data in the biomedical domains. Thanks to such large-scale translation, we introduce ViPubmedT5, a pretrained-Decoder Transformer model trained on 20 million abstracts from the high-quality public corpus.
• Score: 1.6347851388527643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Biomedical data and benchmarks are highly valuable yet very limited in low-resource languages other than English such as Vietnamese. In this paper, we make use of a state-of-the-art translation model in English-Vietnamese to translate and produce both pretrained as well as 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.

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