@inproceedings{deng-etal-2026-sage,
title = "{S}ag{E}-{QG}: Skill-Aligned and Evidence-Grounded Educational Question Generation via Counterfactual Evidence Reinforcement Learning",
author = "Deng, Yuqiu and
Zhang, Chaoli and
Liu, Chenyu and
Wei, Xingjun and
Zhou, Xinliang and
Wang, Zhiguang and
Wang, Yanfeng and
Li, Haoyang and
Wen, Qingsong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.424/",
pages = "8920--8937",
ISBN = "979-8-89176-462-0",
abstract = "Automatic Question Generation (QG) can make reading-comprehension practice and assessment scalable. For educational use, generated questions should diagnose a target comprehension skill and rely on the passage evidence an expert would use to write the question. Existing answer-aware and skill-conditioned QG methods improve controllability, but often leave these skill-to-question and evidence-to-question dependencies implicit. We present SagE-QG, a Skill-aligned and Evidence-Grounding Framework for Question Generation. SagE-QG exposes evidence-grounded trajectories, optimizes multi-aspect rewards for answerability, skill alignment, and human similarity, and applies Counterfactual Evidence Intervention in reinforcement learning. The intervention removes selected evidence and rewards the Interventional Grounding Gap between full-passage and masked-evidence generations. Experiments on three reading-comprehension QG datasets show that SagE-QG delivers stronger educational quality, especially in skill alignment and answerability, while achieving competitive reference-based results."
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<abstract>Automatic Question Generation (QG) can make reading-comprehension practice and assessment scalable. For educational use, generated questions should diagnose a target comprehension skill and rely on the passage evidence an expert would use to write the question. Existing answer-aware and skill-conditioned QG methods improve controllability, but often leave these skill-to-question and evidence-to-question dependencies implicit. We present SagE-QG, a Skill-aligned and Evidence-Grounding Framework for Question Generation. SagE-QG exposes evidence-grounded trajectories, optimizes multi-aspect rewards for answerability, skill alignment, and human similarity, and applies Counterfactual Evidence Intervention in reinforcement learning. The intervention removes selected evidence and rewards the Interventional Grounding Gap between full-passage and masked-evidence generations. Experiments on three reading-comprehension QG datasets show that SagE-QG delivers stronger educational quality, especially in skill alignment and answerability, while achieving competitive reference-based results.</abstract>
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%0 Conference Proceedings
%T SagE-QG: Skill-Aligned and Evidence-Grounded Educational Question Generation via Counterfactual Evidence Reinforcement Learning
%A Deng, Yuqiu
%A Zhang, Chaoli
%A Liu, Chenyu
%A Wei, Xingjun
%A Zhou, Xinliang
%A Wang, Zhiguang
%A Wang, Yanfeng
%A Li, Haoyang
%A Wen, Qingsong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F deng-etal-2026-sage
%X Automatic Question Generation (QG) can make reading-comprehension practice and assessment scalable. For educational use, generated questions should diagnose a target comprehension skill and rely on the passage evidence an expert would use to write the question. Existing answer-aware and skill-conditioned QG methods improve controllability, but often leave these skill-to-question and evidence-to-question dependencies implicit. We present SagE-QG, a Skill-aligned and Evidence-Grounding Framework for Question Generation. SagE-QG exposes evidence-grounded trajectories, optimizes multi-aspect rewards for answerability, skill alignment, and human similarity, and applies Counterfactual Evidence Intervention in reinforcement learning. The intervention removes selected evidence and rewards the Interventional Grounding Gap between full-passage and masked-evidence generations. Experiments on three reading-comprehension QG datasets show that SagE-QG delivers stronger educational quality, especially in skill alignment and answerability, while achieving competitive reference-based results.
%U https://aclanthology.org/2026.findings-emnlp.424/
%P 8920-8937
Markdown (Informal)
[SagE-QG: Skill-Aligned and Evidence-Grounded Educational Question Generation via Counterfactual Evidence Reinforcement Learning](https://aclanthology.org/2026.findings-emnlp.424/) (Deng et al., Findings 2026)
ACL
- Yuqiu Deng, Chaoli Zhang, Chenyu Liu, Xingjun Wei, Xinliang Zhou, Zhiguang Wang, Yanfeng Wang, Haoyang Li, and Qingsong Wen. 2026. SagE-QG: Skill-Aligned and Evidence-Grounded Educational Question Generation via Counterfactual Evidence Reinforcement Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 8920–8937, Budapest, Hungary. Association for Computational Linguistics.