@inproceedings{papadopoulos-etal-2023-andronicus,
title = "Andronicus of Rhodes at {S}em{E}val-2023 Task 4: Transformer-Based Human Value Detection Using Four Different Neural Network Architectures",
author = "Papadopoulos, Georgios and
Kokol, Marko and
Dagioglou, Maria and
Petasis, Georgios",
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-1.75",
doi = "10.18653/v1/2023.semeval-1.75",
pages = "542--548",
abstract = "This paper presents our participation to the {``}Human Value Detection shared task (Kiesel et al., 2023), as {``}Andronicus of Rhodes. We describe the approaches behind each entry in the official evaluation, along with the motivation behind each approach. Our best-performing approach has been based on BERT large, with 4 classification heads, implementing two different classification approaches (with different activation and loss functions), and two different partitioning of the training data, to handle class imbalance. Classification is performed through majority voting. The proposed approach outperforms the BERT baseline, ranking in the upper half of the competition.",
}
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<abstract>This paper presents our participation to the “Human Value Detection shared task (Kiesel et al., 2023), as “Andronicus of Rhodes. We describe the approaches behind each entry in the official evaluation, along with the motivation behind each approach. Our best-performing approach has been based on BERT large, with 4 classification heads, implementing two different classification approaches (with different activation and loss functions), and two different partitioning of the training data, to handle class imbalance. Classification is performed through majority voting. The proposed approach outperforms the BERT baseline, ranking in the upper half of the competition.</abstract>
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%0 Conference Proceedings
%T Andronicus of Rhodes at SemEval-2023 Task 4: Transformer-Based Human Value Detection Using Four Different Neural Network Architectures
%A Papadopoulos, Georgios
%A Kokol, Marko
%A Dagioglou, Maria
%A Petasis, Georgios
%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 papadopoulos-etal-2023-andronicus
%X This paper presents our participation to the “Human Value Detection shared task (Kiesel et al., 2023), as “Andronicus of Rhodes. We describe the approaches behind each entry in the official evaluation, along with the motivation behind each approach. Our best-performing approach has been based on BERT large, with 4 classification heads, implementing two different classification approaches (with different activation and loss functions), and two different partitioning of the training data, to handle class imbalance. Classification is performed through majority voting. The proposed approach outperforms the BERT baseline, ranking in the upper half of the competition.
%R 10.18653/v1/2023.semeval-1.75
%U https://aclanthology.org/2023.semeval-1.75
%U https://doi.org/10.18653/v1/2023.semeval-1.75
%P 542-548
Markdown (Informal)
[Andronicus of Rhodes at SemEval-2023 Task 4: Transformer-Based Human Value Detection Using Four Different Neural Network Architectures](https://aclanthology.org/2023.semeval-1.75) (Papadopoulos et al., SemEval 2023)
ACL