Title: A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education Show Chinese title

Title: 一种可扩展且自动的主题控制问答生成的新方法

Abstract: The development of Automatic Question Generation (QG) models has the potential to significantly improve educational practices by reducing the teacher workload associated with creating educational content. This paper introduces a novel approach to educational question generation that controls the topical focus of questions. The proposed Topic-Controlled Question Generation (T-CQG) method enhances the relevance and effectiveness of the generated content for educational purposes. Our approach uses fine-tuning on a pre-trained T5-small model, employing specially created datasets tailored to educational needs. The research further explores the impacts of pre-training strategies, quantisation, and data augmentation on the model's performance. We specifically address the challenge of generating semantically aligned questions with paragraph-level contexts, thereby improving the topic specificity of the generated questions. In addition, we introduce and explore novel evaluation methods to assess the topical relatedness of the generated questions. Our results, validated through rigorous offline and human-backed evaluations, demonstrate that the proposed models effectively generate high-quality, topic-focused questions. These models have the potential to reduce teacher workload and support personalised tutoring systems by serving as bespoke question generators. With its relatively small number of parameters, the proposals not only advance the capabilities of question generation models for handling specific educational topics but also offer a scalable solution that reduces infrastructure costs. This scalability makes them feasible for widespread use in education without reliance on proprietary large language models like ChatGPT. Show Chinese abstract

Abstract: 自动问题生成（QG）模型的发展有望通过减少与创建教育内容相关的教师工作量，显著改善教育实践。 本文介绍了一种新的教育问题生成方法，该方法控制问题的主题焦点。 所提出的主题控制问题生成（T-CQG）方法提高了生成内容的相关性和教育目的的有效性。 我们的方法在预训练的T5-small模型上进行微调，使用专门为教育需求定制的数据集。 研究进一步探讨了预训练策略、量化和数据增强对模型性能的影响。 我们特别解决了生成与段落级上下文语义对齐的问题的挑战，从而提高了生成问题的主题特异性。 此外，我们引入并探索了新颖的评估方法，以评估生成问题的主题相关性。 我们的结果通过严格的离线和人工支持的评估得到验证，表明所提出的模型能够有效生成高质量、主题聚焦的问题。 这些模型有潜力通过作为定制的问题生成器来减少教师的工作量，并支持个性化辅导系统。 由于参数数量相对较少，这些提案不仅增强了问题生成模型处理特定教育主题的能力，还提供了一个可扩展的解决方案，降低了基础设施成本。 这种可扩展性使它们在教育中广泛使用成为可能，而无需依赖像ChatGPT这样的专有大型语言模型。