@inproceedings{zhao-etal-2022-educational,
title = "Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization",
author = "Zhao, Zhenjie and
Hou, Yufang and
Wang, Dakuo and
Yu, Mo and
Liu, Chengzhong and
Ma, Xiaojuan",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.348",
doi = "10.18653/v1/2022.acl-long.348",
pages = "5073--5085",
abstract = "Generating educational questions of fairytales or storybooks is vital for improving children{'}s literacy ability. However, it is challenging to generate questions that capture the interesting aspects of a fairytale story with educational meaningfulness. In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. To train the event-centric summarizer, we finetune a pre-trained transformer-based sequence-to-sequence model using silver samples composed by educational question-answer pairs. On a newly proposed educational question-answering dataset FairytaleQA, we show good performance of our method on both automatic and human evaluation metrics. Our work indicates the necessity of decomposing question type distribution learning and event-centric summary generation for educational question generation.",
}
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<abstract>Generating educational questions of fairytales or storybooks is vital for improving children’s literacy ability. However, it is challenging to generate questions that capture the interesting aspects of a fairytale story with educational meaningfulness. In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. To train the event-centric summarizer, we finetune a pre-trained transformer-based sequence-to-sequence model using silver samples composed by educational question-answer pairs. On a newly proposed educational question-answering dataset FairytaleQA, we show good performance of our method on both automatic and human evaluation metrics. Our work indicates the necessity of decomposing question type distribution learning and event-centric summary generation for educational question generation.</abstract>
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%0 Conference Proceedings
%T Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization
%A Zhao, Zhenjie
%A Hou, Yufang
%A Wang, Dakuo
%A Yu, Mo
%A Liu, Chengzhong
%A Ma, Xiaojuan
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhao-etal-2022-educational
%X Generating educational questions of fairytales or storybooks is vital for improving children’s literacy ability. However, it is challenging to generate questions that capture the interesting aspects of a fairytale story with educational meaningfulness. In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. To train the event-centric summarizer, we finetune a pre-trained transformer-based sequence-to-sequence model using silver samples composed by educational question-answer pairs. On a newly proposed educational question-answering dataset FairytaleQA, we show good performance of our method on both automatic and human evaluation metrics. Our work indicates the necessity of decomposing question type distribution learning and event-centric summary generation for educational question generation.
%R 10.18653/v1/2022.acl-long.348
%U https://aclanthology.org/2022.acl-long.348
%U https://doi.org/10.18653/v1/2022.acl-long.348
%P 5073-5085
Markdown (Informal)
[Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization](https://aclanthology.org/2022.acl-long.348) (Zhao et al., ACL 2022)
ACL