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

TL;DR

This paper introduces a new dataset and task for understanding and reconstructing implicit warrants in arguments, highlighting the challenges current models face in capturing implicit reasoning.

Contribution

It develops a scalable crowdsourcing methodology to create a dataset for argument warrant reconstruction and formulates a new challenging comprehension task.

Findings

Current neural models struggle with the task

The dataset contains 2,000 authentic arguments

Reconstruction of implicit warrants remains difficult for AI

Abstract

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. 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.