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SemEval-2018 Task 12

The Argument Reasoning Comprehension Task

verfasst von
Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, Benno Stein

A natural language argument is composed of a claim as well as reasons given as premises for the claim. The warrant explaining the reasoning is usually left implicit, as it is clear from the context and common sense. This makes a comprehension of arguments easy for humans but hard for machines. This paper summarizes the first shared task on argument reasoning comprehension. Given a premise and a claim along with some topic information, the goal is to automatically identify the correct warrant among two candidates that are plausible and lexically close, but in fact imply opposite claims. We describe the dataset with 1970 instances that we built for the task, and we outline the 21 computational approaches that participated, most of which used neural networks. The results reveal the complexity of the task, with many approaches hardly improving over the random accuracy of ≈ 0.5. Still, the best observed accuracy (0.712) underlines the principle feasibility of identifying warrants. Our analysis indicates that an inclusion of external knowledge is key to reasoning comprehension.

Externe Organisation(en)
Technische Universität Darmstadt
Bauhaus-Universität Weimar
Aufsatz in Konferenzband
Anzahl der Seiten
ASJC Scopus Sachgebiete
Theoretische Informatik und Mathematik, Sprache und Linguistik, Linguistik und Sprache
Elektronische Version(en) (Zugang: Offen)