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

TL;DR

This paper introduces entity-centric question generation, a new task for generating questions from an entity perspective, and proposes a PLM-based framework with novel modules that outperforms baselines.

Contribution

The paper presents a new task ECQG, a large-scale dataset, and a novel GenCONE framework with content focusing and question verification modules.

Findings

GenCONE outperforms baseline models.

Content focusing and question verification modules are effective.

The dataset supports open-domain entity-centric question generation.

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.