Stab, Christian ; Gurevych, Iryna (2017)
Recognizing Insufficiently Supported Arguments in Argumentative Essays.
Valencia, Spain
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Stab, Christian ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Recognizing Insufficiently Supported Arguments in Argumentative Essays |
| Sprache: | Englisch |
| Publikationsjahr: | April 2017 |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017) |
| Veranstaltungsort: | Valencia, Spain |
| URL / URN: | http://www.aclweb.org/anthology/E17-1092 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | UKP_reviewed;UKP_a_ArMin |
| ID-Nummer: | TUD-CS-2017-0010 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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