Question generation (QG) from a given context can enhance comprehension, engagement, assessment, and overall efficacy in learning or conversational environments. Despite recent advancements in QG, the challenge of enhancing or measuring the diversity of generated questions often remains unaddressed. In this paper, we introduce a multi-question generation model (mQG), which is capable of generating multiple, diverse, and answerable questions by focusing on context and questions. To validate the answerability of the generated questions, we employ a SQuAD2.0 fine-tuned question answering model, classifying the questions as answerable or not. We train and evaluate mQG on the FairytaleQA dataset, a well-structured QA dataset based on storybooks, with narrative questions. We further apply a zero-shot adaptation on the TellMeWhy and SQuAD1.1 datasets. mQG shows promising results across various evaluation metrics, among strong baselines.

翻译：从给定上下文中生成问题（QG）可以增强学习或对话环境中的理解、参与度、评估和整体效果。尽管问题生成技术最近取得了进展，但增强或衡量生成问题多样性的挑战往往仍未得到解决。在本文中，我们提出了一种多问题生成模型（mQG），该模型能够通过关注上下文和问题来生成多个多样化、可回答的问题。为了验证生成问题的可回答性，我们采用了一个基于SQuAD2.0微调的问题回答模型，将问题分类为可回答或不可回答。我们在FairytaleQA数据集（一个基于故事书的结构化问答数据集，包含叙事性问题）上训练和评估mQG。此外，我们在TellMeWhy和SQuAD1.1数据集上进行了零样本适应。mQG在多种评估指标上表现出有前景的结果，优于强基线模型。