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
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 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.- Anthology ID:
- 2023.eacl-main.228
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3131–3142
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.228
- DOI:
- Cite (ACL):
- Long Phan, Tai Dang, Hieu Tran, Trieu H. Trinh, Vy Phan, Lam D. Chau, and Minh-Thang Luong. 2023. Enriching Biomedical Knowledge for Low-resource Language Through Large-scale Translation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3131–3142, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Enriching Biomedical Knowledge for Low-resource Language Through Large-scale Translation (Phan et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2023.eacl-main.228.pdf