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        <title>Ask To The Point: Open-Domain Entity-Centric Question Generation</title>
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    <name type="personal">
        <namePart type="given">Yuxiang</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
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            <namePart type="given">Juan</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. The task aims to generate questions from an entity perspective. To solve ECQG, we propose a coherent PLM-based framework GenCONE with two novel modules: content focusing and question verification. The content focusing module first identifies a focus as “what to ask” to form draft questions, and the question verification module refines the questions afterwards by verifying the answerability. We also construct a large-scale open-domain dataset from SQuAD to support this task. Our extensive experiments demonstrate that GenCONE significantly and consistently outperforms various baselines, and two modules are effective and complementary in generating high-quality questions.</abstract>
    <identifier type="citekey">liu-etal-2023-ask</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.178</identifier>
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        <date>2023-12</date>
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            <start>2703</start>
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%0 Conference Proceedings
%T Ask To The Point: Open-Domain Entity-Centric Question Generation
%A Liu, Yuxiang
%A Huang, Jie
%A Chang, Kevin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-ask
%X We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. The task aims to generate questions from an entity perspective. To solve ECQG, we propose a coherent PLM-based framework GenCONE with two novel modules: content focusing and question verification. The content focusing module first identifies a focus as “what to ask” to form draft questions, and the question verification module refines the questions afterwards by verifying the answerability. We also construct a large-scale open-domain dataset from SQuAD to support this task. Our extensive experiments demonstrate that GenCONE significantly and consistently outperforms various baselines, and two modules are effective and complementary in generating high-quality questions.
%R 10.18653/v1/2023.findings-emnlp.178
%U https://aclanthology.org/2023.findings-emnlp.178
%U https://doi.org/10.18653/v1/2023.findings-emnlp.178
%P 2703-2716

Markdown (Informal)

[Ask To The Point: Open-Domain Entity-Centric Question Generation](https://aclanthology.org/2023.findings-emnlp.178) (Liu et al., Findings 2023)

• Ask To The Point: Open-Domain Entity-Centric Question Generation (Liu et al., Findings 2023)

ACL

• Yuxiang Liu, Jie Huang, and Kevin Chang. 2023. Ask To The Point: Open-Domain Entity-Centric Question Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2703–2716, Singapore. Association for Computational Linguistics.