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Biomedical question-answering (QA) has gained increased attention for itscapability to provide users with high-quality information from a vastscientific literature. Although an increasing number of biomedical QA datasetshas been recently made available, those resources are still rather limited andexpensive to produce. Transfer learning via pre-trained language models (LMs)has been shown as a promising approach to leverage existing general-purposeknowledge. However, finetuning these large models can be costly and timeconsuming, often yielding limited benefits when adapting to specific themes ofspecialised domains, such as the COVID-19 literature. To bootstrap furthertheir domain adaptation, we propose a simple yet unexplored approach, which wecall biomedical entity-aware masking (BEM). We encourage masked language modelsto learn entity-centric knowledge based on the pivotal entities characterizingthe domain at hand, and employ those entities to drive the LM fine-tuning. Theresulting strategy is a downstream process applicable to a wide variety ofmasked LMs, not requiring additional memory or components in the neuralarchitectures. Experimental results show performance on par withstate-of-the-art models on several biomedical QA datasets.

Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies