@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks
%A Eo, Sugyeong
%A Moon, Hyeonseok
%A Kim, Jinsung
%A Hur, Yuna
%A Kim, Jeongwook
%A Lee, SongEun
%A Chun, Changwoo
%A Park, Sungsoo
%A Lim, Heuiseok
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F eo-etal-2023-towards
%X 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.
%R 10.18653/v1/2023.findings-acl.380
%U https://aclanthology.org/2023.findings-acl.380
%U https://doi.org/10.18653/v1/2023.findings-acl.380
%P 6100-6115
Markdown (Informal)
[Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks](https://aclanthology.org/2023.findings-acl.380) (Eo et al., Findings 2023)
ACL
- Sugyeong Eo, Hyeonseok Moon, Jinsung Kim, Yuna Hur, Jeongwook Kim, SongEun Lee, Changwoo Chun, Sungsoo Park, and Heuiseok Lim. 2023. Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6100–6115, Toronto, Canada. Association for Computational Linguistics.