Diversity Enhanced Narrative Question Generation for Storybooks
This work addresses the challenge of enhancing question diversity for educational applications like storybook comprehension, but it appears incremental as it builds on existing QG methods with a focus on diversity.
The paper tackled the problem of generating diverse and answerable questions from storybook contexts by introducing a multi-question generation model (mQG), which showed promising results on the FairytaleQA dataset and zero-shot adaptation to other datasets.
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.