@inproceedings{beigman-klebanov-etal-2017-detecting,
    title = "Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron?",
    author = "Beigman Klebanov, Beata  and
      Gyawali, Binod  and
      Song, Yi",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P17-2038/",
    doi = "10.18653/v1/P17-2038",
    pages = "244--249",
    abstract = "Automatic identification of good arguments on a controversial topic has applications in civics and education, to name a few. While in the civics context it might be acceptable to create separate models for each topic, in the context of scoring of students' writing there is a preference for a single model that applies to all responses. Given that good arguments for one topic are likely to be irrelevant for another, is a single model for detecting good arguments a contradiction in terms? We investigate the extent to which it is possible to close the performance gap between topic-specific and across-topics models for identification of good arguments."
}