Diversity Enhanced Narrative Question Generation for Storybooks
Hokeun Yoon JinYeong Bak
Question generation (QG) from a given context can enhance comprehension,
engagement, assessment, and overall efficacy in learning or conversational
environments. Despite recent advancements in QG, the challenge of enhancing or
measuring the diversity of generated questions often remains unaddressed. In
this paper, we introduce a multi-question generation model (mQG), which is
capable of generating multiple, diverse, and answerable questions by focusing
on context and questions. To validate the answerability of the generated
questions, we employ a SQuAD2.0 fine-tuned question answering model,
classifying the questions as answerable or not. We train and evaluate mQG on
the FairytaleQA dataset, a well-structured QA dataset based on storybooks, with
narrative questions. We further apply a zero-shot adaptation on the TellMeWhy
and SQuAD1.1 datasets. mQG shows promising results across various evaluation
metrics, among strong baselines.