@inproceedings{persing-ng-2017-lightly,
    title = "Lightly-Supervised Modeling of Argument Persuasiveness",
    author = "Persing, Isaac  and
      Ng, Vincent",
    editor = "Kondrak, Greg  and
      Watanabe, Taro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/I17-1060/",
    pages = "594--604",
    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."
}