Ask To The Point: Open-Domain Entity-Centric Question Generation
Yuxiang Liu Jie Huang Kevin Chen-Chuan Chang
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.