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

Habernal, Ivan ; Wachsmuth, Henning ; Gurevych, Iryna ; Stein, Benno (2018)
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants.
New Orleans, USA
Conference or Workshop Item, Bibliographie

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Habernal, Ivan ; Wachsmuth, Henning ; Gurevych, Iryna ; Stein, Benno
Type of entry: Bibliographie
Title: The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Language: English
Date: June 2018
Publisher: Association for Computational Linguistics
Book Title: Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Event Location: New Orleans, USA
URL / URN: http://aclweb.org/anthology/N18-1175
Corresponding Links:
Uncontrolled Keywords: UKP_a_ArMin;UKP_p_ArguAna
Identification Number: TUD-CS-2018-0029
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.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Date Deposited: 16 Feb 2018 08:51
Last Modified: 24 Jan 2020 12:03
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