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

Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno :
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants.
[Online-Edition: http://aclweb.org/anthology/N18-1175]
Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Association for Computational Linguistics
[Conference or Workshop Item] , (2018)

Official URL: http://aclweb.org/anthology/N18-1175
Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno
Title: The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Language: English
Title of Book: Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Publisher: Association for Computational Linguistics
Uncontrolled Keywords: UKP_a_ArMin;UKP_p_ArguAna
Divisions: Department of Computer Science
Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Location: New Orleans, USA
Date Deposited: 16 Feb 2018 08:51
Official URL: http://aclweb.org/anthology/N18-1175
Additional Information:

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.

Identification Number: TUD-CS-2018-0029
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