Computational Argumentation Quality Assessment in Natural Language
Computational Argumentation Quality Assessment in Natural Language
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DOI：
10.18653/v1/e17-1017
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Henning Wachsmuth;Nona Naderi;Yufang Hou;Yonatan Bilu;Vinodkumar Prabhakaran;Tim Alberdingk Thijm;Graeme Hirst;Benno Stein
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文献类型：
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作者：
Henning Wachsmuth;Nona Naderi;Yufang Hou;Yonatan Bilu;Vinodkumar Prabhakaran;Tim Alberdingk Thijm;Graeme Hirst;Benno Stein
计算论证的研究面临着如何自动评估论点或论证质量的问题。尽管在自然语言处理中已经达到了不同的质量维度，但对论证质量的共同理解仍然缺失。本文介绍了自然语言中关于计算论证质量的首次整体工作。我们全面调查了评估逻辑，修辞和辩证质量维度的各种现有理论和方法，并从中得出了系统的分类学。此外，我们为分类法中所有15个维度提供了320个论点，并提供了320个论点。我们的结果为计算论证质量评估的研究建立了共同点。
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.