| Challenge: | Recent studies on open-book QG have achieved promising progress, but generating natural questions under a more practical closed-book setting remains a challenge. |
| Approach: | They propose a QG model that stores more information in its parameters through contrastive learning and an answer reconstruction module. |
| Outcome: | The proposed model outperforms baselines in automatic evaluation and human evaluation on a public dataset and a new WikiCQA dataset. |
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Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
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Contrastive Multi-document Question Generation (2021.eacl-main)
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Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
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Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (2020.acl-main)
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| Challenge: | Question Generation is a simple syntactic transformation but many aspects of semantics influence what questions are good to form. |
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Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering (2025.acl-long)
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| Challenge: | Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights. |
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Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? (2021.acl-long)
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| Challenge: | Existing work is limited in using small benchmarks with high test-train overlaps. |
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Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference (2023.findings-acl)
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Let Me Know What to Ask: Interrogative-Word-Aware Question Generation (D19-58)
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| Challenge: | Existing models focus on generating questions based on text and the answer to the generated question. |
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Improving Question Generation With to the Point Context (D19-1)
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| Challenge: | Existing sequence-to-sequence neural models may not be able to identify answer-relevant context words for question generation. |
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