Method of question answering relation detection in military field based on pre-training model
In order to solve the problem of relationship link error of user query statements in military domain question answering service and improve the accuracy of knowledge base question answering,a relationship detection method based on pre-trained language model was proposed,in which the entity name information in user questions was abandoned and binding ontology information was added.On this basis,the relationship detection model combined with the pre-trained language model embedded attention mechanism was studied.The above relationship detection method was applied to the military knowledge base question answering task in combination with the military corpus.The experimental results show that,on one hand,the constrained ontology information is added to expand the ontology level information and the ontology knowledge topology,and the relationship detection results are con-strained,resulting in an increase of about 6.2％of the accuracy of the test relationship link;on the other hand,more background knowledge is injected into military data through the pre-trained lan-guage model,which increases the training accuracy by about 10％compared with the unembedded pre-trained language model.It indicates that combined with the information characteristics of the know-ledge base,the practical application effect of relationship detection in military domain question answering service is enhanced.
relation detectionattention mechanismbinding ontologypre-trained language model
万方数据
维普
