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
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
594–604
Language:
URL:
https://aclanthology.org/I17-1060
DOI:
Bibkey:
Cite (ACL):
Isaac Persing and Vincent Ng. 2017. Lightly-Supervised Modeling of Argument Persuasiveness. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 594–604, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Lightly-Supervised Modeling of Argument Persuasiveness (Persing & Ng, IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-1060.pdf