mlsanityData to 7 Oct 2026

It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books

arXiv 2109.03423 ↗First posted 8 Sep 2021cs.CL cs.AIcited by 23

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Abstract (arXiv)

Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions they should ask, in order to help students to improve their narrative understanding capabilities. We design an automated question-answer generation (QAG) system for this education scenario: given a story book at the kindergarten to eighth-grade level as input, our system can automatically generate QA pairs that are capable of testing a variety of dimensions of a student's comprehension skills. Our proposed QAG model architecture is demonstrated using a new expert-annotated FairytaleQA dataset, which has 278 child-friendly storybooks with 10,580 QA pairs. Automatic and human evaluations show that our model outperforms state-of-the-art QAG baseline systems. On top of our QAG system, we also start to build an interactive story-telling application for the future real-world deployment in this educational scenario.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 21 of its 38 references that are in the map. First posted on arXiv 8 Sep 2021.

Builds on

21 of 38 references.

References · 21

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  6. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  7. 2018CoQA: A Conversational Question Answering Challengecited by 674
  8. 2019Unified Language Model Pre-training for Natural Language Understanding and Generationcited by 668
  9. 2017The NarrativeQA Reading Comprehension Challengecited by 590
  10. 2017SearchQA: A New Q&A Dataset Augmented with Context from a Search Enginecited by 221
  11. 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 198
  12. 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
9 more
  1. 2015The Goldilocks Principle: Reading Children's Books with Explicit Memory Representationscited by 108
  2. 2019Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Modelcited by 102
  3. 2017Neural Question Generation from Text: A Preliminary Studycited by 78
  4. 2020End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systemscited by 65
  5. 2018Towards a Better Metric for Evaluating Question Generation Systemscited by 52
  6. 2017Question Answering and Question Generation as Dual Taskscited by 37
  7. 2020Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answeringcited by 17
  8. 2021Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Studycited by 15
  9. 2017A Joint Model for Question Answering and Question Generationcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 14; 2024: 1; 2025: 4; 2026: 1.

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  1. 2022Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehensioncited by 79
  2. 2022QAmeleon: Multilingual QA with Only 5 Examplescited by 25
  3. 2023RELIC: Investigating Large Language Model Responses using Self-Consistencycited by 23
  4. 2023PaniniQA: Enhancing Patient Education Through Interactive Question Answeringcited by 21
  5. 2023Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanationscited by 13
  6. 2023Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rankcited by 12
  7. 2023Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecturecited by 10
  8. 2023Diversify Question Generation with Retrieval-Augmented Style Transfercited by 10
  9. 2022Closed-book Question Generation via Contrastive Learningcited by 6
  10. 2023Towards Diverse and Effective Question-Answer Pair Generation from Children Storybookscited by 5
  11. 2023Diversity Enhanced Narrative Question Generation for Storybookscited by 5
  12. 2023EHRTutor: Enhancing Patient Understanding of Discharge Instructionscited by 4
11 more
  1. 2025Chatbot To Help Patients Understand Their Healthcited by 4
  2. 2023Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Talescited by 3
  3. 2023Are Large Language Models Fit For Guided Reading?cited by 2
  4. 2025Can LLMs Generate and Solve Linguistic Olympiad Puzzles?cited by 2
  5. 2023SkillQG: Learning to Generate Question for Reading Comprehension Assessmentcited by 1
  6. 2023UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimizationcited by 1
  7. 2024What Would You Ask When You First Saw a2+b2=c2? Evaluating LLM on Curiosity-Driven Questioningcited by 1
  8. 2023'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalismcited by 0
  9. 2025Question Generation for Assessing Early Literacy Reading Comprehensioncited by 0
  10. 2025RoboBuddy in the Classroom: Exploring LLM-Powered Social Robots for Storytelling in Learning and Integration Activitiescited by 0
  11. 2026LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answeringcited by 0