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.

本文介绍了一种新的神经网络结构，特别用于法语语法空填练习的生成任务，并提供了一份用于标准化评估的真实基准数据集，实现了平均F1得分82%

从部分标注的数据中学习：面向示例的语言学习填空练习创建