Since performing exercises (including, e.g., practice tests) forms a crucial component of learning, and creating such exercises requires non-trivial effort from the teacher. There is a great value in automatic exercise generation in digital tools in education. In this paper, we particularly focus on automatic creation of gapfilling exercises for language learning, specifically grammar exercises. Since providing any annotation in this domain requires human expert effort, we aim to avoid it entirely and explore the task of converting existing texts into new gap-filling exercises, purely based on an example exercise, without explicit instruction or detailed annotation of the intended grammar topics. We contribute (i) a novel neural network architecture specifically designed for aforementioned gap-filling exercise generation task, and (ii) a real-world benchmark dataset for French grammar. We show that our model for this French grammar gap-filling exercise generation outperforms a competitive baseline classifier by 8% in F1 percentage points, achieving an average F1 score of 82%. Our model implementation and the dataset are made publicly available to foster future research, thus offering a standardized evaluation and baseline solution of the proposed partially annotated data prediction task in grammar exercise creation.

翻译：由于完成练习（包括模拟测试等）是学习过程的关键环节，而设计此类练习需要教师投入大量精力，因此在教育数字化工具中实现练习自动生成具有重要价值。本文聚焦语言学习领域（特别是语法练习）的填空练习自动生成任务。由于该领域任何标注工作都需耗费人类专家精力，我们试图完全规避人工标注，探索如何仅基于示例练习（无需显式指令或对目标语法主题的详细标注）将现有文本转化为新的填空练习。本文的贡献包括：(i) 专为上述填空练习生成任务设计的新型神经网络架构；(ii) 面向法语语法的真实基准数据集。实验表明，我们提出的法语语法填空练习生成模型在F1分数上较有竞争力的基线分类器高出8个百分点，平均F1值达82%。为促进后续研究，我们公开了模型实现与数据集，从而为语法练习创建中提出的部分标注数据预测任务提供标准化评估与基线解决方案。