@inproceedings{cheng-etal-2021-guiding,
title = "Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting",
author = "Cheng, Yi and
Li, Siyao and
Liu, Bang and
Zhao, Ruihui and
Li, Sujian and
Lin, Chenghua and
Zheng, Yefeng",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.465",
doi = "10.18653/v1/2021.acl-long.465",
pages = "5968--5978",
abstract = "This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.",
}
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%0 Conference Proceedings
%T Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting
%A Cheng, Yi
%A Li, Siyao
%A Liu, Bang
%A Zhao, Ruihui
%A Li, Sujian
%A Lin, Chenghua
%A Zheng, Yefeng
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F cheng-etal-2021-guiding
%X This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.
%R 10.18653/v1/2021.acl-long.465
%U https://aclanthology.org/2021.acl-long.465
%U https://doi.org/10.18653/v1/2021.acl-long.465
%P 5968-5978
Markdown (Informal)
[Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting](https://aclanthology.org/2021.acl-long.465) (Cheng et al., ACL-IJCNLP 2021)
ACL