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

• URL: http://arxiv.org/abs/2102.08366v1
• Date: Tue, 16 Feb 2021 18:51:13 GMT
• Title: Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
• Authors: Gabriele Pergola, Elena Kochkina, Lin Gui, Maria Liakata, Yulan He
• Abstract summary: Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature. We propose a simple yet unexplored approach, which we call biomedical entity-aware masking (BEM) We encourage masked language models to learn entity-centric knowledge based on the pivotal entities characterizing the domain at hand, and employ those entities to drive the LM fine-tuning. Experimental results show performance on par with state-of-the-art models on several biomedical QA datasets.
• Score: 25.990479833023166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature. Although an increasing number of biomedical QA datasets has been recently made available, those resources are still rather limited and expensive to produce. Transfer learning via pre-trained language models (LMs) has been shown as a promising approach to leverage existing general-purpose knowledge. However, finetuning these large models can be costly and time consuming, often yielding limited benefits when adapting to specific themes of specialised domains, such as the COVID-19 literature. To bootstrap further their domain adaptation, we propose a simple yet unexplored approach, which we call biomedical entity-aware masking (BEM). We encourage masked language models to learn entity-centric knowledge based on the pivotal entities characterizing the domain at hand, and employ those entities to drive the LM fine-tuning. The resulting strategy is a downstream process applicable to a wide variety of masked LMs, not requiring additional memory or components in the neural architectures. Experimental results show performance on par with state-of-the-art models on several biomedical QA datasets.

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