@inproceedings{yu-etal-2025-generating,
title = "Generating Commonsense Reasoning Questions with Controllable Complexity through Multi-step Structural Composition",
author = "Yu, Jianxing and
Wang, Shiqi and
Lai, Hanjiang and
Chen, Wenqing and
Rao, Yanghui and
Su, Qinliang and
Yin, Jian",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.155/",
pages = "2261--2276",
abstract = "This paper studies the task of generating commonsense reasoning questions (QG) with desired difficulty levels. Compared to traditional shallow questions that can be solved by simple term matching, ours are more challenging. Our answering process requires reasoning over multiple contextual and commonsense clues. That involves advanced comprehension skills, such as abstract semantics learning and missing knowledge inference. Existing work mostly learns to map the given text into questions, lacking a mechanism to control results with the desired complexity. To address this problem, we propose a novel controllable framework. We first derive contextual and commonsense clues involved in reasoning questions from the text. These clues are used to create simple sub-questions. We then aggregate multiple sub-questions to compose complex ones under the guidance of prior reasoning structures. By iterating this process, we can compose a complex QG task based on a series of smaller and simpler QG subtasks. Each subtask serves as a building block for a larger one. Each composition corresponds to an increase in the reasoning step. Moreover, we design a voting verifier to ensure results' validity from multiple views, including answer consistency, reasoning difficulty, and context correlation. Finally, we can learn the optimal QG model to yield thought-provoking results. Evaluations on two typical datasets validate our method."
}
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<abstract>This paper studies the task of generating commonsense reasoning questions (QG) with desired difficulty levels. Compared to traditional shallow questions that can be solved by simple term matching, ours are more challenging. Our answering process requires reasoning over multiple contextual and commonsense clues. That involves advanced comprehension skills, such as abstract semantics learning and missing knowledge inference. Existing work mostly learns to map the given text into questions, lacking a mechanism to control results with the desired complexity. To address this problem, we propose a novel controllable framework. We first derive contextual and commonsense clues involved in reasoning questions from the text. These clues are used to create simple sub-questions. We then aggregate multiple sub-questions to compose complex ones under the guidance of prior reasoning structures. By iterating this process, we can compose a complex QG task based on a series of smaller and simpler QG subtasks. Each subtask serves as a building block for a larger one. Each composition corresponds to an increase in the reasoning step. Moreover, we design a voting verifier to ensure results’ validity from multiple views, including answer consistency, reasoning difficulty, and context correlation. Finally, we can learn the optimal QG model to yield thought-provoking results. Evaluations on two typical datasets validate our method.</abstract>
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%0 Conference Proceedings
%T Generating Commonsense Reasoning Questions with Controllable Complexity through Multi-step Structural Composition
%A Yu, Jianxing
%A Wang, Shiqi
%A Lai, Hanjiang
%A Chen, Wenqing
%A Rao, Yanghui
%A Su, Qinliang
%A Yin, Jian
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F yu-etal-2025-generating
%X This paper studies the task of generating commonsense reasoning questions (QG) with desired difficulty levels. Compared to traditional shallow questions that can be solved by simple term matching, ours are more challenging. Our answering process requires reasoning over multiple contextual and commonsense clues. That involves advanced comprehension skills, such as abstract semantics learning and missing knowledge inference. Existing work mostly learns to map the given text into questions, lacking a mechanism to control results with the desired complexity. To address this problem, we propose a novel controllable framework. We first derive contextual and commonsense clues involved in reasoning questions from the text. These clues are used to create simple sub-questions. We then aggregate multiple sub-questions to compose complex ones under the guidance of prior reasoning structures. By iterating this process, we can compose a complex QG task based on a series of smaller and simpler QG subtasks. Each subtask serves as a building block for a larger one. Each composition corresponds to an increase in the reasoning step. Moreover, we design a voting verifier to ensure results’ validity from multiple views, including answer consistency, reasoning difficulty, and context correlation. Finally, we can learn the optimal QG model to yield thought-provoking results. Evaluations on two typical datasets validate our method.
%U https://aclanthology.org/2025.coling-main.155/
%P 2261-2276
Markdown (Informal)
[Generating Commonsense Reasoning Questions with Controllable Complexity through Multi-step Structural Composition](https://aclanthology.org/2025.coling-main.155/) (Yu et al., COLING 2025)
ACL