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Writing strong arguments can be challenging for learners. It requires toselect and arrange multiple argumentative discourse units (ADUs) in a logicaland coherent way as well as to decide which ADUs to leave implicit, so calledenthymemes. However, when important ADUs are missing, readers might not be ableto follow the reasoning or understand the argument's main point. This paperintroduces 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. Westudy how corpora for these tasks can be created automatically by deleting ADUsfrom an argumentative text that are central to the argument and its quality,while maintaining the text's naturalness. Based on the ICLEv3 corpus ofargumentative learner essays, we create 40,089 argument instances for enthymemedetection and reconstruction. Through manual studies, we provide evidence thatthe proposed corpus creation process leads to the desired quality reduction,and results in arguments that are similarly natural to those written bylearners. Finally, first baseline approaches to enthymeme detection andreconstruction demonstrate the corpus' usefulness.

Mind the Gap: Automated Corpus Creation for Enthymeme Detection and Reconstruction in Learner Arguments