@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://aclanthology.org/2023.semeval-1.74",
doi = "10.18653/v1/2023.semeval-1.74",
pages = "532--541",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models
%A Schroter, Daniel
%A Dementieva, Daryna
%A Groh, Georg
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F schroter-etal-2023-adam
%X 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.
%R 10.18653/v1/2023.semeval-1.74
%U https://aclanthology.org/2023.semeval-1.74
%U https://doi.org/10.18653/v1/2023.semeval-1.74
%P 532-541
Markdown (Informal)
[Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models](https://aclanthology.org/2023.semeval-1.74) (Schroter et al., SemEval 2023)
ACL