Assessing Convincingness of Arguments in Online Debates with Limited Number of Features

Lisa Andreevna Chalaguine, Claudia Schulz


Abstract
We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016). Rather than using argument specific feature values, we measure feature values relative to the average value in the debate, allowing us to determine argument convincingness with fewer features (between 5 and 35) than normally used for natural language processing tasks. We use a simple forward-feeding neural network for this task and achieve an accuracy of 0.77 which is comparable to the accuracy obtained using 64k features and a support vector machine by Habernal and Gurevych.
Anthology ID:
E17-4008
Volume:
Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
75–83
Language:
URL:
https://www.aclweb.org/anthology/E17-4008
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-4008.pdf