@inproceedings{heinrich-etal-2025-multi,
    title = "Multi-Class versus Means-End: Assessing Classification Approaches for Argument Patterns",
    author = "Heinrich, Maximilian  and
      Khatib, Khalid Al  and
      Stein, Benno",
    editor = "Chistova, Elena  and
      Cimiano, Philipp  and
      Haddadan, Shohreh  and
      Lapesa, Gabriella  and
      Ruiz-Dolz, Ramon",
    booktitle = "Proceedings of the 12th Argument mining Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.argmining-1.19/",
    doi = "10.18653/v1/2025.argmining-1.19",
    pages = "195--204",
    ISBN = "979-8-89176-258-9",
    abstract = "In the study of argumentation, the schemes introduced by Walton et al. (2008) represent a significant advancement in understanding and analyzing the structure and function of arguments. Walton{'}s framework is particularly valuable for computational reasoning, as it facilitates the identification of argument patterns and the reconstruction of enthymemes. Despite its practical utility, automatically identifying these schemes remains a challenging problem. To aid human annotators, Visser et al. (2021) developed a decision tree for scheme classification. Building on this foundation, we propose a means-end approach to argument scheme classification that systematically leverages expert knowledge{---}encoded in a decision tree{---}to guide language models through a complex classification task. We assess the effectiveness of the means-end approach by conducting a comprehensive comparison with a standard multi-class approach across two datasets, applying both prompting and supervised learning methods to each approach. Our results indicate that the means-end approach, when combined with supervised learning, achieves scores only slightly lower than those of the multi-class classification approach. At the same time, the means-end approach enhances explainability by identifying the specific steps in the decision tree that pose the greatest challenges for each scheme{---}offering valuable insights for refining the overall means-end classification process."
}