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Biomedical Question Answering aims to obtain an answer to the given questionfrom the biomedical domain. Due to its high requirement of biomedical domainknowledge, it is difficult for the model to learn domain knowledge from limitedtraining data. We propose a contextual embedding method that combinesopen-domain QA model \aoa and \biobert model pre-trained on biomedical domaindata. We adopt unsupervised pre-training on large biomedical corpus andsupervised fine-tuning on biomedical question answering dataset. Additionally,we adopt an MLP-based model weighting layer to automatically exploit theadvantages of two models to provide the correct answer. The public dataset\biomrc constructed from PubMed corpus is used to evaluate our method.Experimental results show that our model outperforms state-of-the-art system bya large margin.

Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering