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        <title>Diversity Enhanced Narrative Question Generation for Storybooks</title>
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    <abstract>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 SQuAD 2.0 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.</abstract>
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%0 Conference Proceedings
%T Diversity Enhanced Narrative Question Generation for Storybooks
%A Yoon, Hokeun
%A Bak, JinYeong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yoon-bak-2023-diversity
%X 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 SQuAD 2.0 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.
%R 10.18653/v1/2023.emnlp-main.31
%U https://aclanthology.org/2023.emnlp-main.31
%U https://doi.org/10.18653/v1/2023.emnlp-main.31
%P 465-482

Markdown (Informal)

[Diversity Enhanced Narrative Question Generation for Storybooks](https://aclanthology.org/2023.emnlp-main.31) (Yoon & Bak, EMNLP 2023)

• Diversity Enhanced Narrative Question Generation for Storybooks (Yoon & Bak, EMNLP 2023)

ACL

• Hokeun Yoon and JinYeong Bak. 2023. Diversity Enhanced Narrative Question Generation for Storybooks. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 465–482, Singapore. Association for Computational Linguistics.