@inproceedings{06ca2ba867de43a1969535e3be0f4bdc,
title = "Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models",
abstract = "This paper presents the best-performing approach alias {"}Adam Smith{"} for the SemEval-2023 Task 4: {"}Identification of Human Values behind Arguments{"}. 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 ({"}Nahj al-Balagha{"}). 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.",
author = "Daniel Schroter and Daryna Dementieva and Georg Groh",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 17th International Workshop on Semantic Evaluation, SemEval 2023, co-located with the 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 ; Conference date: 13-07-2023 Through 14-07-2023",
year = "2023",
language = "English",
series = "17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop",
publisher = "Association for Computational Linguistics",
pages = "532--541",
editor = "Ojha, {Atul Kr.} and Dogruoz, {A. Seza} and {Da San Martino}, Giovanni and Madabushi, {Harish Tayyar} and Ritesh Kumar and Elisa Sartori",
booktitle = "17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop",
}