Biomedical extractive question answering based on dynamic routing and answer voting

Many existing biomedical extractive question answering methods are based on pre-trained models, which do not take full advantage of the hidden layer knowledge of pretrained models and do not consider span overlap between answers when predicting. To address these issues, we propose a new question answering model, called ALBERT with Dynamic Routing and Answer Voting (ADRAV). The ADRAV can reasonably utilize hidden layer knowledge through dynamic routing, and consider span similarity between answers through answer voting. To improve the performance of the model, we also carry out pre-fine-tuning, and add a dynamic parameter adjustment mechanism in the process of pre-fine-tuning. Experimental results show that our model achieves significant performance improvement with fewer parameters on BioASQ 4b, 5b, 6b, 9b, and outperforms SOTA baselines on BioASQ 4b, 6b.