基于全局覆盖机制与表示学习的生成式知识问答技术

Aiming at the problem of low answer accuracy caused by unfamiliar words in the existing generative question answering model and the problem of vocabulary repetition caused by pattern confusion, this paper proposes a method of introducing knowledge representation learning results to improve the model＇s ability to recognize unfamiliar words and improve the accuracy of the model. At the same time, this paper proposes to use a global coverage mechanism to balance the probability of answer generation in different modes, reduce the repeated output problem caused by the confusion of prediction modes, and improve the quality of the answer. Based on the knowledge question answering model, this paper combines the inference results of knowledge representation learning, so that the model has the ability to answer fuzzy answers. Experiments on synthetic datasets and real-world datasets demonstrate that this model can effectively improve the quality of generated answers and can provide fuzzy answers to reasoning knowledge.