@inproceedings{arampatzis-etal-2023-duth,
    title = "{DUTH} at {S}em{E}val-2023 Task 9: An Ensemble Approach for {T}witter Intimacy Analysis",
    author = "Arampatzis, Giorgos  and
      Perifanis, Vasileios  and
      Symeonidis, Symeon  and
      Arampatzis, Avi",
    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-1.170",
    doi = "10.18653/v1/2023.semeval-1.170",
    pages = "1225--1230",
    abstract = "This work presents the approach developed by the DUTH team for participating in the SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis. Our results show that pre-processing techniques do not affect the learning performance for the task of multilingual intimacy analysis. In addition, we show that fine-tuning a transformer-based model does not provide advantages over using the pre-trained model to generate text embeddings and using the resulting representations to train simpler and more efficient models such as MLP. Finally, we utilize an ensemble of classifiers, including three MLPs with different architectures and a CatBoost model, to improve the regression accuracy.",
}

This work presents the approach developed by the DUTH team for participating in the SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis. Our results show that pre-processing techniques do not affect the learning performance for the task of multilingual intimacy analysis. In addition, we show that fine-tuning a transformer-based model does not provide advantages over using the pre-trained model to generate text embeddings and using the resulting representations to train simpler and more efficient models such as MLP. Finally, we utilize an ensemble of classifiers, including three MLPs with different architectures and a CatBoost model, to improve the regression accuracy.
Arampatzis, Giorgos Perifanis, Vasileios Symeonidis, Symeon Arampatzis, Avi
DUTH at SemEval-2023 Task 9: An Ensemble Approach for Twitter Intimacy Analysis