  Writing strong arguments can be challenging for learners. It requires to
select and arrange multiple argumentative discourse units (ADUs) in a logical
and coherent way as well as to decide which ADUs to leave implicit, so called
enthymemes. However, when important ADUs are missing, readers might not be able
to follow the reasoning or understand the argument's main point. This paper
introduces two new tasks for learner arguments: to identify gaps in arguments
(enthymeme detection) and to fill such gaps (enthymeme reconstruction).
Approaches to both tasks may help learners improve their argument quality. We
study how corpora for these tasks can be created automatically by deleting ADUs
from an argumentative text that are central to the argument and its quality,
while maintaining the text's naturalness. Based on the ICLEv3 corpus of
argumentative learner essays, we create 40,089 argument instances for enthymeme
detection and reconstruction. Through manual studies, we provide evidence that
the proposed corpus creation process leads to the desired quality reduction,
and results in arguments that are similarly natural to those written by
learners. Finally, first baseline approaches to enthymeme detection and
reconstruction demonstrate the corpus' usefulness.
