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：练习（包括模拟测试等）是学习过程中的关键环节，而设计此类练习需要教师投入大量精力，因此教育数字化工具中自动生成练习的功能具有重要价值。本文重点针对语言学习中的语法填空练习自动生成问题展开研究。由于该领域的标注工作需要领域专家投入人力，我们旨在完全避免人工标注，探索如何仅基于示例练习，在没有明确指令或目标语法主题详细标注的条件下，将现有文本转化为新的填空练习。本文贡献包括：(i) 一种专为上述填空练习生成任务设计的新型神经网络架构，以及(ii) 一个面向法语语法的真实场景基准数据集。实验表明，针对法语语法填空练习生成任务，我们的模型在F1分数上比竞争性基线分类器高出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代码与数据集，从而为语法练习生成中的部分标注数据预测任务提供了标准化评估基准与基线解决方案。