@inproceedings{eo-etal-2023-towards,
    title = "Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks",
    author = "Eo, Sugyeong  and
      Moon, Hyeonseok  and
      Kim, Jinsung  and
      Hur, Yuna  and
      Kim, Jeongwook  and
      Lee, SongEun  and
      Chun, Changwoo  and
      Park, Sungsoo  and
      Lim, Heuiseok",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.380",
    doi = "10.18653/v1/2023.findings-acl.380",
    pages = "6100--6115",
    abstract = "Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. However, the diversity of QA types remains a challenge despite its contributions to comprehensive learning and assessment of children. In this paper, we propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. Our framework comprises a QFS-based answer generator, an iterative QA generator, and a relevancy-aware ranker. The two generators aim to expand the number of candidates while covering various types. The ranker trained on the in-context negative samples clarifies the top-N outputs based on the ranking score. Extensive evaluations and detailed analyses demonstrate that our approach outperforms previous state-of-the-art results by significant margins, achieving improved diversity and quality. Our task-oriented processes are consistent with real-world demand, which highlights our system{'}s high applicability.",
}

Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. However, the diversity of QA types remains a challenge despite its contributions to comprehensive learning and assessment of children. In this paper, we propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. Our framework comprises a QFS-based answer generator, an iterative QA generator, and a relevancy-aware ranker. The two generators aim to expand the number of candidates while covering various types. The ranker trained on the in-context negative samples clarifies the top-N outputs based on the ranking score. Extensive evaluations and detailed analyses demonstrate that our approach outperforms previous state-of-the-art results by significant margins, achieving improved diversity and quality. Our task-oriented processes are consistent with real-world demand, which highlights our system{'}s high applicability.
Eo, Sugyeong Moon, Hyeonseok Kim, Jinsung Hur, Yuna Kim, Jeongwook Lee, SongEun Chun, Changwoo Park, Sungsoo Lim, Heuiseok
Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks