Question Generation Model Based on Text Knowledge Enhancement
Pre-trained language models,which are trained on large-scale datasets with extensive computing power,can extract significant amounts of knowledge from unstructured text data.To address the limited information in current triplets,a method is proposed that utilizes pre-trained language models to enrich this knowledge.Initially,a textual knowledge generator is designed to enhance the semantics of the triplets by leveraging the extensive knowledge embedded in the pre-trained models.This generator transforms the information within the triplets into subgraph descriptions.Subsequently,a question type predictor is employed to determine the appropriate question words.These question words are essential for question generation as they help to locate the domain of the answer accurately,resulting in semantically coherent questions and enhanced control over the generation process.Finally,a controlled generation framework is developed to ensure that both key entities and question words appear in the generated questions,thereby increasing the accuracy of these questions.The efficacy of the proposed model is demonstrated on the public datasets WebQuestion and PathQuestion.When compared to the existing model LFKQG,the proposed model shows improvements in the BLUE-4,METEOR,and ROUGE-L metrics by 0.28,0.16,and 0.22 percentage points,respectively,on the WebQuestion dataset,and by 0.8,0.39,and 0.46 percentage points,respectively,on the PathQuestion dataset.
natural language understandingquestion generationKnowledge Graph(KG)pre-trained language modelknowledge enhancement
万方数据
维普
