Abstract:
"Automatic generation of questions is a challenging task in Natural Language Processing
that has attracted a lot of attention in recent years. Creating a variety of question
categories, such as open-ended, true/false, and multiple-choice questions, that cover
different aspects of the paragraph’s content remains a challenge. In addition, existing
methods have a tendency to generate questions that are identical in structure and
content, resulting in a lack of diversity that could impact the accuracy of comprehension
assessment. In order to improve comprehension assessment, an innovative approach is
required that can generate a wide variety of question types while promoting question
diversity."
