CLASSIFICATION OF MULTI-TASK QUESTIONS IN THE AUTOMATIC QUESTION-ANSWER SYSTEM FOR TOURISM

At present, the tourism industry information construction needs to construct the tourism automatic question and answer system, in which the questions classification is a significant part of the question and answer system, the traditional question category system angle is single, and the traditional classification model is not good for the unbalanced question dataset. To solve the above situation, this paper constructs the architecture of question category in tourism field from two angles: question theme and question answer type. And it proposed multi-task question classification model MT-Bert, conducted multi-task training on Bert, added self-attention mechanism, used Softmax classifier, and designed multi-task fusion loss function. The results on tourism DataSet in Shanxi show that the micro average F1 values of MT-Bert in the two kinds of systems are 97.6% and 91.7% respectively, and the prediction failure of unbalanced data is avoided, so the unbalanced data can be processed effectively.