Ask To The Point: Open-Domain Entity-Centric Question Generation
October 21, 2023 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Yuxiang Liu, Jie Huang, Kevin Chen-Chuan Chang
arXiv ID
2310.14126
Category
cs.CL: Computation & Language
Cross-listed
cs.AI
Citations
6
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
5 months ago
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.
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