Towards Diverse and Effective Question-Answer Pair Generation from
  Children Storybooks
Sugyeong Eo Hyeonseok Moon Jinsung Kim Yuna Hur Jeongwook Kim Songeun Lee Changwoo Chun Sungsoo Park Heuiseok Lim
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.