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Recognizing Insufficiently Supported Arguments in Argumentative Essays

Stab, Christian and Gurevych, Iryna (2017):
Recognizing Insufficiently Supported Arguments in Argumentative Essays.
In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), Association for Computational Linguistics, Valencia, Spain, [Online-Edition: http://www.aclweb.org/anthology/E17-1092],
[Conference or Workshop Item]

Abstract

In this paper, we propose a new task for assessing the quality of natural language arguments. The premises of a well-reasoned argument should provide enough evidence for accepting or rejecting its claim. Although this criterion, known as sufficiency, is widely adopted in argumentation theory, there are no empirical studies on its applicability to real arguments. In this work, we show that human annotators substantially agree on the sufficiency criterion and introduce a novel annotated corpus. Furthermore, we experiment with feature-rich SVMs and convolutional neural networks and achieve 84% accuracy for automatically identifying insufficiently supported arguments. The final corpus as well as the annotation guideline are freely available for encouraging future research on argument quality.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Stab, Christian and Gurevych, Iryna
Title: Recognizing Insufficiently Supported Arguments in Argumentative Essays
Language: English
Abstract:

In this paper, we propose a new task for assessing the quality of natural language arguments. The premises of a well-reasoned argument should provide enough evidence for accepting or rejecting its claim. Although this criterion, known as sufficiency, is widely adopted in argumentation theory, there are no empirical studies on its applicability to real arguments. In this work, we show that human annotators substantially agree on the sufficiency criterion and introduce a novel annotated corpus. Furthermore, we experiment with feature-rich SVMs and convolutional neural networks and achieve 84% accuracy for automatically identifying insufficiently supported arguments. The final corpus as well as the annotation guideline are freely available for encouraging future research on argument quality.

Title of Book: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017)
Publisher: Association for Computational Linguistics
Uncontrolled Keywords: UKP_reviewed;UKP_a_ArMin
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
Event Location: Valencia, Spain
Date Deposited: 31 Dec 2016 14:29
Official URL: http://www.aclweb.org/anthology/E17-1092
Identification Number: TUD-CS-2017-0010
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