Abstract: With the rapid development of the Internet and big data,artificial intelligence,represented by deep neural network(DNN),has ushered in a golden period of development.As an important branch in the field of artificial intelligence,question answering has attracted more and more scholars’ attention.The existing deep neural network module can extract the semantic features of the question or answer,however,on the one hand,it ignores the semantic relation between the question and answer,on the other hand,it cannot grasp the potential relation among all the characters in the question or answer as a whole.Therefore,two different forms of attention interaction module,namely cross-embedding and self-embedding,are used to solve the above pro-blems,and a set of deep learning model based on the proposed attention interaction module is designed to prove the effectiveness of this attention interaction module.Firstly,each character in the question and answer is mapped into a fixed length vector,and the corresponding character embedding matrix is obtained respectively.After that,the character embedding matrix is sent into the attentional interaction module to obtain the character embedding matrix that takes all characters of the question and answer into account.After adding the previous character embedding matrix,it is sent into the deep neural network module to extract the semantic features of the question and answer.Finally,the vector representations of the question and the answer are obtained,and the similarity between them is calculated.Experiments show that the accuracy of Top-1 of the proposed model is 3.55 % higher than that of the mainstream deep learning model at most,which proves the effectiveness of the proposed attention interaction module in resolving the above problems.

Key words: Artificial intelligence, Question answering, Deep learning, Attention, Character embedding