Attention-based BRCNN for Chinese Medical Question Answering

Recently, the development of automatic question answering in the field of smart medicine has attracted much attention, especially in the field of Chinese medicine. Many methods have been presented for Chinese medical question answering. However, due to redundant information in sentences, existing Chinese medical question answering methods, like BRCNN, are difficult to choose the best answer for patients’ questions. Aiming at the problem, this paper proposes a BiGRU model based on attention mechanism, and adds it to BRCNN. Specifically, our algorithm first inputs the word vector generated by BERT into the attention module to get the attention of the sentence. Secondly, the attention and word vector of the sentence are input into BiGRU together, and the sequence information of the sentence is obtained through the interaction of attention and word vector. Thirdly, the sequence information of sentences is used as the input of multi-scale CNNs to obtain n-gram information of different sizes of sentences. Finally, the best answer to the question is selected by calculating the cosine similarity between the question and the answer. We conducted some experiments to verify the proposed model by comparing it with the baseline model on the cMedQA and cMedQA2 data sets. The results show that the accuracy of the proposed model is 0.5% and 0.2% higher than that of the baseline model, respectively.