Abstract
Injecting external domain-specific knowledge (e.g., UMLS) into pretrained language models (LMs) advances their capability to handle specialised in-domain tasks such as biomedical entity linking (BEL). However, such abundant expert knowledge is available only for a handful of languages (e.g., English). In this work, by proposing a novel cross-lingual biomedical entity linking task (XL-BEL) and establishing a new XL-BEL benchmark spanning 10 typologically diverse languages, we first investigate the ability of standard knowledge-agnostic as well as knowledge-enhanced monolingual and multilingual LMs beyond the standard monolingual English BEL task. The scores indicate large gaps to English performance. We then address the challenge of transferring domain-specific knowledge in resource-rich languages to resource-poor ones. To this end, we propose and evaluate a series of cross-lingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge to languages with little to no in-domain data. Remarkably, we show that our proposed domain-specific transfer methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language, and without any in-domain parallel data.- Anthology ID:
- 2021.acl-short.72
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 565–574
- Language:
- URL:
- https://aclanthology.org/2021.acl-short.72
- DOI:
- 10.18653/v1/2021.acl-short.72
- Cite (ACL):
- Fangyu Liu, Ivan Vulić, Anna Korhonen, and Nigel Collier. 2021. Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 565–574, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking (Liu et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2021.acl-short.72.pdf
- Code
- cambridgeltl/sapbert
- Data
- XL-BEL