Lightly-Supervised Modeling of Argument Persuasiveness
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.
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