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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.178.pdf