Medical Answer Selection Based on Two Attention Mechanisms with BiRNN

The contradiction between the large population of China and the limited medical resources lead to the difficulty of getting medical services. The emergence of question answering (QA) system in the medical field allows people to receive timely treatment at home and alleviates the burden on hospitals and doctors. To this end, this paper proposes a new model called Att-BiRNN-Att which combines the Bidirectional RNN (Recurrent Neural Network) with two attention mechanisms. The model employs BiRNN to capture more information in the context instead of the traditional directional RNN. Also, two attention mechanisms are used in the model to produce better feature representation of the answer. One attention is used before the input of BiRNN, and the other is used after the output of BiRNN. The combination of two attentions makes full use of the relevant information between the answer and question. The experiment on the HealthTap medical QA dataset shows that our model outperforms four state-of-theart deep learning models, which confirm the effectiveness of Att-BiRNN-Att model.