mlsanityData to 7 Oct 2026

A Feasibility Study of Answer-Agnostic Question Generation for Education

arXiv 2203.08685 ↗First posted 16 Mar 2022cs.CL cs.AI cs.HCcited by 6

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Abstract (arXiv)

We conduct a feasibility study into the applicability of answer-agnostic question generation models to textbook passages. We show that a significant portion of errors in such systems arise from asking irrelevant or uninterpretable questions and that such errors can be ameliorated by providing summarized input. We find that giving these models human-written summaries instead of the original text results in a significant increase in acceptability of generated questions (33% rightarrow 83%) as determined by expert annotators. We also find that, in the absence of human-written summaries, automatic summarization can serve as a good middle ground.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 37 references that are in the map. First posted on arXiv 16 Mar 2022.

Builds on

13 of 37 references.

References · 13

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  4. 2015Pointer Networkscited by 882
  5. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  6. 2019Unified Language Model Pre-training for Natural Language Understanding and Generationcited by 668
  7. 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 221
  8. 2020UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Trainingcited by 204
  9. 2019Synthetic QA Corpora Generation with Roundtrip Consistencycited by 143
  10. 2018Towards a Better Metric for Evaluating Question Generation Systemscited by 52
  11. 2018Harvesting Paragraph-Level Question-Answer Pairs from Wikipediacited by 49
  12. 2021Improving Unsupervised Question Answering via Summarization-Informed Question Generationcited by 21
1 more
  1. 2021OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approachcited by 7

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Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 2.

6 papers.

  1. 2023Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rankcited by 12
  2. 2022MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languagescited by 11
  3. 2023Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomycited by 11
  4. 2024AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approachcited by 11
  5. 2022Closed-book Question Generation via Contrastive Learningcited by 6
  6. 2024DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehensioncited by 0