Comparison of template-based and multilayer perceptron-based approach for automatic question generation system

Abstract

Intelligent tutoring systems are computer-assisted learning systems with adaption to students using artificial intelligence tools. An intelligent tutoring system can drastically improve education efficiency as it provides solutions to many issues that now plague the educational industry. One important component in education is questioning learners to assess and reinforce learning. This research compares two approaches for automatic question generation, a template-based question generation strategy and the phrase-Level automatic question generation system utilizing the Multilayer perceptron model. A template-based technique is a baseline for automatic question generation that uses templates taken from the training set to generate questions by filling certain templates with specific topic items. We utilize question-answer sentence composition datasets and manually constructed datasets for our experiments and comparison of the Multilayer perceptron training model. We used both human and automatic evaluation metrics to assess the efficiency of our suggested methods. Regarding automatic metrics, we selected the BLEU-n gram and ROUGE-N methodologies. The evaluation results demonstrate that the phrase-level Multilayer perceptron-based strategy dominates the template-based approach and has a promising score in both ROUGE automatic and human evaluation metrics.