Question And Answer System Based On Deep Attention Model

With the development of Internet technology,artificial intelligence has become a hot topic,and the question and answer system is also a popular research direction in this field.The development of network technology has also brought about an explosive growth in the amount of information on the entire Internet.Question and answer system can help users find what they need from the enormous information.At the same time,in the field of e-commerce,the study of intelligent question and answer system can help improve the accuracy of intelligent customer service.Therefore,the study of question and system has great practical application value.At present,most researches on traditional question and answer system are based on retrieval and matching.This method has the disadvantages of poor adaptability to different scenarios and the inability to capture the above semantic information.Thus,it has no migration.So,in order to solve the shortcomings which mentioned above,this paper proposes a question and answer system that based on deep learning and attention model.This paper mainly proposes to use word2 vec to get the semantic features of text sentences,and uses Bi-directional Long Short Term Memory to obtain the timing information of sentences.Meanwhile,in order to obtain the topic information of the text sentence,an attention model is fused during the whole model training.After the pre-processing of collected data,a corresponding question and answer document is established.The training is performed with a sequence-to-sequence framework to obtain a matching relationship between question and answer sentences,and then a matching answer can be generated for a question entered by the user.In the experiment,this paper uses the N-gram model to systematically evaluate the experimental results.By calculating the ratio of the number of different 2-gram in the generated answers to the total 2-gram in the data set,the ratios of 2-gram reflect the semantic diversity of the generated answers.At the same time,the control experiment is set up to compare and analyze the experimental results based on the traditional retrieval technology and the Bi-LSTM network question and answer system.The final experimental results show that the model proposed in this paper,which fuses the attention mechanism and Bi-LSTM,can effectively improve the accuracy of the answer.