Lightly-Supervised Modeling of Argument Persuasiveness

Isaac Persing and Vincent Ng.
Proceedings of the 8th International Joint Conference on Natural Language Processing, pp. 594-604, 2017.

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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.

BibTeX entry

@InProceedings{Persing+Ng:17b,
  author = {Isaac Persing and Vincent Ng},
  title = {Lightly-Supervised Modeling of Argument Persuasiveness},
  booktitle = {Proceedings of the 8th International Joint Conference on Natural Language Processing},
  pages = {594--604},
  year = 2017}