The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. 1 On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.

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Filling the gap between the claim and premises (aka reasons) of a natural language argument empirically remains an open issue, due to the inherent difficulty of reconstructing the world knowledge and reasoning patterns in arguments.

Our main hypothesis is that, even if there is no limit to the tacit length of the reasoning chain between claims and premises, it is possible to systematically reconstruct a meaningful warrant, depending only on what we take as granted and what needs to be explicit.

What makes comprehending and analyzing arguments hard is that claims and warrants are usually implicit (Freeman, 2011, p. 82). As they are ‘taken for granted’ by the arguer, the reader has to infer the contextually most relevant content that she believes the arguer intended to use. To this end, the reader relies on common sense knowledge (Oswald, 2016; Wilson and Sperber, 2004).