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.

该研究介绍了两个新学习者论证任务：识别论证中的缺陷（省略式推理检测）和填补这些缺陷（省略式推理重建）。通过从学习者的论证文本中删除对于论证和质量至关重要的论证单元（ADUs）的方法，我们创建了一个用于省略式推理检测和重建的语料库，以便帮助学习者提高其论证质量。同时，我们验证了所提出的语料库创建过程导致了所需的质量降低，并得到了与学习者撰写的论证类似自然的论证结果。最后，基线方法的省略式推理检测和重建表明了该语料库的实用性。