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.

翻译：撰写强有力的论证对学习者而言具有挑战性。这不仅需要以逻辑连贯的方式选择和排列多个论证性话语单元，还需决定哪些话语单元应隐含表达（即所谓的省略三段论）。然而，当关键论证单元缺失时，读者可能无法理解推理过程或把握论证主旨。本文提出了学习者论证的两项新任务：识别论证中的缺失环节（省略三段论检测）及填补此类缺失（省略三段论重建）。针对这两项任务的方法有望帮助学习者提升论证质量。我们研究了如何通过从论证文本中删除对论证及其质量至关重要的话语单元（同时保持文本自然性）来自动构建相关语料库。基于ICLEv3学习者论证作文语料库，我们创建了40,089个用于省略三段论检测与重建的论证实例。通过人工研究，我们验证了所提出的语料库构建流程能实现预期的质量降级，并生成与学习者所写论证自然度相当的文本。最后，针对省略三段论检测与重建的初步基线方法验证了该语料库的实用性。