Computational Argumentation Quality Assessment in Natural Language

被引：0

作者：

Wachsmuth, Henning ^{[1
]}

Naderi, Nona ^{[2
]}

Hou, Yufang ^{[3
]}

Buhl, Yonatan ^{[4
]}

Prabhakaran, Vinodkumar ^{[5
]}

Thijm, Tim Alberdingk ^{[2
]}

Hirst, Graeme ^{[2
]}

Stein, Benno ^{[1
]}

机构：

[1] Bauhaus Univ Weimar, Weimar, Germany

[2] Univ Toronto, Toronto, ON, Canada

[3] IBM Res, Dublin, Ireland

[4] IBM Res, Haifa, Israel

[5] Stanford Univ, Stanford, CA 94305 USA

来源：

15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS | 2017年

基金：

加拿大自然科学与工程研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment.

引用

页码：176 / 187

页数：12

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