---
title: "R2DQG: A Quality Meets Diversity Framework for Question Generation over Knowledge Bases"
authors:
  - "Yimeng Ren"
  - "Yanhua Yu"
  - "Lizi Liao"
  - "Yuhu Shang"
  - "Kangkang Lu"
  - "Mingliang Yan"
doi: "10.24963/ijcai.2025/915"
url: "https://ijcai.org/proceedings/2025/915"
pdf: "https://ijcai.org/proceedings/2025/0915.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "8231-8240"
track: "Main Track"
keywords:
  - "Natural Language Processing: NLP: Question answering"
  - "Data Mining: DM: Knowledge graphs and knowledge base completion"
  - "Natural Language Processing: NLP: Language generation"
  - "Natural Language Processing: NLP: Summarization"
---

# R2DQG: A Quality Meets Diversity Framework for Question Generation over Knowledge Bases

Yimeng Ren, Yanhua Yu, Lizi Liao, Yuhu Shang, Kangkang Lu, Mingliang Yan

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 8231-8240. DOI: [10.24963/ijcai.2025/915](https://doi.org/10.24963/ijcai.2025/915)

## Abstract

The task of Knowledge-Based Question Generation (KBQG) involves generating natural language questions from structured knowledge sources, posing unique challenges in balancing linguistic diversity and semantic relevance. Existing models often focus on maximizing surface-level similarity to ground-truth questions, neglecting the need for diverse syntactic forms and leading to semantic drift during generation. To overcome these challenges, we propose Refine-Reinforced Diverse Question Generation (R2DQG), a two-phase framework leveraging a generation-then-refinement paradigm. The Generator first constructs a diverse set of expressive templates using dependency parse tree similarity, capturing a wide range of syntactic patterns and styles. These templates guide the creation of question drafts, ensuring both diversity and semantic relevance. In the second phase, a Corrector module refines the drafts to mitigate semantic drift and enhance overall coherence and quality. Experiments on public datasets show that R2DQG outperforms state-of-the-art models in generating diverse, contextually accurate questions. Moreover, synthetic datasets generated by R2DQG enhance downstream QA performance, underscoring the practical utility of our approach.