Mind the Gap: Automated Corpus Creation for Enthymeme Detection and Reconstruction in Learner Arguments (2023)

Abstract: 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.

## introduction argumentative writing is an essential skill that can be challenging to acquire @xcite @xcite . to write a logically strong or cogent argument, a writer has to present sufficient evidence (i.e., the argument's premises) that justifies the argument's claim (i.e., its conclusion). in addition, arguments draw on premises that are left implicit, so called enthymemes @xcite , which may be key to match the expectations of the audience @xcite . however, if a relevant premise is missing, the argument may be insufficient to accept its conclusion @xcite essay topic: discuss the advantages and disadvantages of banning smoking in restaurants. ## automatic corpus creation this section presents our approach to automatic corpus creation. given a base corpus of argumentative learner texts as input, we propose to create enthymematic arguments automatically by remov- ## corpus this section summarizes the corpora that we use for model training and for automatic corpus creation. then, we give details about the composition and statistics of the resulting created corpus. ## corpus analysis this section reports on our evaluation of the effectiveness of our automatic approach for creating inadequate enthymemes that are harmful to the quality of arguments while not making their texts seem unnatural. to this end, we manually assessed (a) the induced reduction in logical argument quality in terms of sufficiency and (b) the naturalness of the enthymematic arguments in comparison to the original arguments written by learners. ## experiments our automatically-created corpus is meant to enable studying two new tasks on learner arguments: enthymeme detection and enthymeme reconstruction. this section presents baselines for the tasks in order to demonstrate the usefulness of the corpus. ## conclusion until now, data for studying enthymemes in learner arguments is neither available nor is it straightforward to write respective arguments in a natural way. therefore, this paper has studied how to automatically create corpora of enthymematic learner arguments using a self-supervised process that carefully removes adus from argumentative texts of highquality while maintaining the texts' naturalness. to be able to point to possibly problematic gaps in learner arguments and make suggestions on how to fill these gaps, we present the tasks of enthymeme detection and reconstruction for learner arguments that can be studied using the created corpus. our analyses provided evidence that our corpus creation approach leads to the desired reduction in logical argument quality while resulting in arguments that are similarly natural to those written by learners. hence, we conclude that, mostly, inadequate enthymemes are created in the sense of missing adus that would complete the logic of a written argument. moreover, first baselines demonstrate that large language models can learn to detect and fill these enthymematic gaps when trained on our corpus. such models may help learners improve the logical quality of their written arguments.