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, Sung Soo Park, Heuiseok Lim · 2023
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.Our code is available at https://github.com/sugyeonge/ Towards-diverse-QAG.git.