@inproceedings{schroter-etal-2023-adam,
    title = "{A}dam-Smith at {S}em{E}val-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models",
    author = "Schroter, Daniel  and
      Dementieva, Daryna  and
      Groh, Georg",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2023.semeval-1.74/",
    doi = "10.18653/v1/2023.semeval-1.74",
    pages = "532--541",
    abstract = "This paper presents the best-performing approach alias {\textquotedblleft}Adam Smith{\textquotedblright} for the SemEval-2023 Task 4: {\textquotedblleft}Identification of Human Values behind Arguments{\textquotedblright}. The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness ({\textquotedblleft}Nahj al-Balagha{\textquotedblright}). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced."
}