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        <title>RUBY: An Effective Framework for Multi-Constraint Multi-Hop Question Generation</title>
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            <title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Wanxiang</namePart>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>Inspired by theories in language psychology, it is natural to consider more constraints, such as intentions, logic, knowledge, etc., when a complex or multi-hop question is generated. As the subtask of Multi-Hop Question Generation (MHQG), the task of Multi-Constraint Multi-Hop Question Generation (MCHQG) is more aligned with human question theories. However, it is hard to determine how to bring various high-dimensional semantic constraints, and how to integrate each constraint across all hops when a multi-hop question is being generating. To address these challenges, we introduce an effective framework which includes constraint dimensionality reduction and divide-and-conquer-based dynamic projection; we call it RUBY. The proposed RUBY contains a module of high-dimensional semantic constraint dimension reduction and a module of sub-question answer pairs-based multi-hop question generation. Meanwhile, a Reasoning Dynamic Projection strategy is tailored to effectively incorporate the constraints into every hop of the multi-hop question. The experimental results demonstrate that RUBY consistently outperforms baseline models, which suggest that RUBY is able to effectively capture and integrate semantic constraints, leading to more accurate and human-like multi-hop question generation. Our code and data are available.</abstract>
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%0 Conference Proceedings
%T RUBY: An Effective Framework for Multi-Constraint Multi-Hop Question Generation
%A Zhao, Wenzhuo
%A Li, Shuangyin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhao-li-2025-ruby
%X Inspired by theories in language psychology, it is natural to consider more constraints, such as intentions, logic, knowledge, etc., when a complex or multi-hop question is generated. As the subtask of Multi-Hop Question Generation (MHQG), the task of Multi-Constraint Multi-Hop Question Generation (MCHQG) is more aligned with human question theories. However, it is hard to determine how to bring various high-dimensional semantic constraints, and how to integrate each constraint across all hops when a multi-hop question is being generating. To address these challenges, we introduce an effective framework which includes constraint dimensionality reduction and divide-and-conquer-based dynamic projection; we call it RUBY. The proposed RUBY contains a module of high-dimensional semantic constraint dimension reduction and a module of sub-question answer pairs-based multi-hop question generation. Meanwhile, a Reasoning Dynamic Projection strategy is tailored to effectively incorporate the constraints into every hop of the multi-hop question. The experimental results demonstrate that RUBY consistently outperforms baseline models, which suggest that RUBY is able to effectively capture and integrate semantic constraints, leading to more accurate and human-like multi-hop question generation. Our code and data are available.
%R 10.18653/v1/2025.acl-long.889
%U https://aclanthology.org/2025.acl-long.889/
%U https://doi.org/10.18653/v1/2025.acl-long.889
%P 18164-18188

Markdown (Informal)

[RUBY: An Effective Framework for Multi-Constraint Multi-Hop Question Generation](https://aclanthology.org/2025.acl-long.889/) (Zhao & Li, ACL 2025)

• RUBY: An Effective Framework for Multi-Constraint Multi-Hop Question Generation (Zhao & Li, ACL 2025)

ACL

• Wenzhuo Zhao and Shuangyin Li. 2025. RUBY: An Effective Framework for Multi-Constraint Multi-Hop Question Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18164–18188, Vienna, Austria. Association for Computational Linguistics.