Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks

TL;DR

This paper presents a novel question-answer pair generation framework from children storybooks that significantly improves diversity and quality, aiding comprehensive learning and assessment in educational contexts.

Contribution

The proposed framework introduces a multi-component system that enhances QA type diversity and answer relevance, outperforming previous methods in quality and applicability.

Findings

Outperforms previous state-of-the-art in diversity and quality

Achieves significant improvements in QA type variety

Demonstrates high applicability in educational settings

Abstract

Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. However, the diversity of QA types remains a challenge despite its contributions to comprehensive learning and assessment of children. In this paper, we propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. Our framework comprises a QFS-based answer generator, an iterative QA generator, and a relevancy-aware ranker. The two generators aim to expand the number of candidates while covering various types. The ranker trained on the in-context negative samples clarifies the top-N outputs based on the ranking score. Extensive evaluations and detailed analyses demonstrate that our approach outperforms previous state-of-the-art results by significant margins, achieving improved diversity and quality. Our task-oriented processes are consistent with real-world demand, which highlights our system's high applicability.