Named entity aware transfer learning for biomedical factoid question answering

Biomedical factoid question answering is an important task in biomedical question answering application. It has attracted much attention because of its reliability of the answer. In question answering system, better representation of word is of much importance and a proper word embedding usually can improve the performance of system significantly. With the success of pre-trained models in general natural language process tasks, pretrained model has been widely used in biomedical area as well and a lot of pretrained model based approaches have been proven effective in biomedical question answering task. Besides the proper word embedding, name entity is also important information for biomedical question answering. Inspired by the concept of transfer learning, in this research we developed a mechanism to finetune BioBERT with name entity dataset to improve the question answering performance. Furthermore, we also apply BiLSTM to encode the question text to obtain sentence level information. To better combine the question level and token level information, we use bagging to further improve the overall performance. The proposed framework has been evaluated on BioASQ 6b and 7b datasets and the results have shown its promising potential.