Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking
This addresses the challenge of applying biomedical entity linking to resource-poor languages, which is incremental as it builds on existing knowledge-enhanced methods.
The paper tackled the problem of cross-lingual biomedical entity linking (XL-BEL) by establishing a benchmark across 10 languages and showing that standard models perform poorly compared to English. They proposed transfer methods using general-domain bitext, achieving gains of up to 20 Precision@1 points without target-language in-domain data.
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.