Lightly-Supervised Modeling of Argument Persuasiveness

Isaac Persing, Vincent Ng


Abstract
We propose the first lightly-supervised approach to scoring an argument’s persuasiveness. Key to our approach is the novel hypothesis that lightly-supervised persuasiveness scoring is possible by explicitly modeling the major errors that negatively impact persuasiveness. In an evaluation on a new annotated corpus of online debate arguments, our approach rivals its fully-supervised counterparts in performance by four scoring metrics when using only 10% of the available training instances.
Anthology ID:
I17-1060
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
594–604
Language:
URL:
https://www.aclweb.org/anthology/I17-1060
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-1060.pdf