@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.",
}

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.
Papadopoulos, Georgios Kokol, Marko Dagioglou, Maria Petasis, Georgios
Andronicus of Rhodes at SemEval-2023 Task 4: Transformer-Based Human Value Detection Using Four Different Neural Network Architectures