Transformer-based Question Text Generation in the Learning System
Jiajun Li, Huazhu Song, Jun Li · 2022
Question text generation from the triple in knowledge graph exists some challenges in learning system. One is the generated question text is difficult to be understood; the other is it considers few contexts. Therefore, this paper focuses on question text generation. Based on the traditional Bi-LSTM+Attention network model, we import Transformer model into question generation to get the simple question with some triples. In addition, this paper proposes a method to get the diverse expressions of questions (a variety of expressions of a question), that is, to take advantage of the semantic similarity algorithm based on Bi-LSTM with the help of a question database constructed in advance. Finally, a corresponding comparison experiment is designed, and the experimental results demonstrated that the accuracy of question generation experiment based on the Transformer model is 8.36% higher than the traditional Bi-LSTM + Attention network model.