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.

翻译：问答对生成（QAG）领域的最新进展使该技术在教育领域中的应用备受关注。然而，尽管问答类型的多样性有助于儿童的全面学习与评估，其多样性问题仍是一大挑战。本文提出了一种QAG框架，通过生成不同的疑问句和隐式/显式答案来增强问答类型的多样性。该框架包含基于QFS的答案生成器、迭代式问答生成器以及关联感知排序器。前两个生成器旨在扩展候选答案的数量并覆盖多种类型，而基于上下文负样本训练的排序器则利用排序得分筛选出最优的N个输出。大量评估和详细分析表明，我们的方法在多样性及质量方面均显著优于此前的最优结果。该任务导向型流程符合实际需求，凸显了系统的高适用性。