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This paper presents the best-performing approach alias "Adam Smith" for theSemEval-2023 Task 4: "Identification of Human Values behind Arguments". Thegoal of the task was to create systems that automatically identify the valueswithin textual arguments. We train transformer-based models until they reachtheir loss minimum or f1-score maximum. Ensembling the models by selecting oneglobal decision threshold that maximizes the f1-score leads to thebest-performing system in the competition. Ensembling based on stacking withlogistic regressions shows the best performance on an additional datasetprovided to evaluate the robustness ("Nahj al-Balagha"). Apart from outliningthe submitted system, we demonstrate that the use of the large ensemble modelis not necessary and that the system size can be significantly reduced.

Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models