Construction and Verification of Type-Controllable Question Generation Model Based on Deep Learning and Knowledge Graphs
Wang Xiaofeng1,Sun Yujie2,Wang Huazhen2(),Zhang Hengzhang2
1The Academy of Chinese Language and Culture Education, Huaqiao University, Xiamen 361021, China 2College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
[Objective] This research aims to automatically generate questions, thereby reducing the workload of manual question generation. It also addresses the issues of uncontrollable question difficulty and limited question dimensions due to collaborative question generation. It encourages learners to engage in-deep reading comprehension with intelligent questions. [Methods] We proposed a question generation model based on Transformer and knowledge graph to automatically generate type-controllable questions. First, we input the knowledge graph into the Graph Transformer module of the TCQG (Type Controllable Question Generation) model for graph representation learning and obtained the subgraph vector. Then, we obtained matching external questions for each subgraph using similarity measures. Next, we input the parameters of 4MAT question type and those external questions into the BiLSTM network for externally enhanced vectors. Finally, we entered the subgraph vector and the externally enhanced vector into the Pointer-Generator Network of the TCQG model to generate questions. [Results] The TCQG model achieves better representation learning of the knowledge graph through the Graph Transformer. The BLEU value is 39.62 on the one-hop triple dataset. In evaluating “what is” questions, the BLEU score is 38.63. Both surpassed the baseline model. [Limitations] This research is limited by the types of questions and cannot cover all types of questions in human language. In addition, this research did not involve matching responses to the questions, which limits its real-world applications. [Conclusions] This research generates diverse, semantically rich, and naturally expressed questions needed in educational scenarios. It enables learners to benefit from the generated questions and engage in deeper reading comprehension.
汪晓凤, 孙雨洁, 王华珍, 张恒彰. 融合深度学习和知识图谱的类型可控问句生成模型构建及验证*[J]. 数据分析与知识发现, 2023, 7(6): 26-37.
Wang Xiaofeng, Sun Yujie, Wang Huazhen, Zhang Hengzhang. Construction and Verification of Type-Controllable Question Generation Model Based on Deep Learning and Knowledge Graphs. Data Analysis and Knowledge Discovery, 2023, 7(6): 26-37.
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