A Learning-Based Framework to Utilize E-HowNet Ontology and Wikipedia Sources to Generate Multiple-Choice Factual Questions

Abstract

This paper proposes a framework that automatically generates multiple-choice questions. Unlike most other similar works that focus on generating questions for English proficiency tests, this paper provides a framework to generate factual questions in Chinese. We have decomposed this problem into several sub-tasks: a) the identification of sentences that contain factual knowledge, b) the identification of the query term from each factual sentence, and c) the generation of distractors. Learning-based approaches are applied to address the first two problems. We then propose a way to generate distractors by using E-How Net ontology database and Wikipedia sources. The system was evaluated through user study and test theory, and achieved a satisfaction rate of up to 70.6%.