Ask To The Point: Open-Domain Entity-Centric Question Generation

Yuxiang Liu, Jie Huang, Kevin Chang


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.
Anthology ID:
2023.findings-emnlp.178
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2703–2716
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.178
DOI:
10.18653/v1/2023.findings-emnlp.178
Bibkey:
Cite (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.
Cite (Informal):
Ask To The Point: Open-Domain Entity-Centric Question Generation (Liu et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-emnlp.178.pdf