  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.
