Multi-Class versus Means-End: Assessing Classification Approaches for Argument Patterns

AbstractIn 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.