@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T DUTH at SemEval-2023 Task 9: An Ensemble Approach for Twitter Intimacy Analysis
%A Arampatzis, Giorgos
%A Perifanis, Vasileios
%A Symeonidis, Symeon
%A Arampatzis, Avi
%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 arampatzis-etal-2023-duth
%X 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.
%R 10.18653/v1/2023.semeval-1.170
%U https://aclanthology.org/2023.semeval-1.170
%U https://doi.org/10.18653/v1/2023.semeval-1.170
%P 1225-1230
Markdown (Informal)
[DUTH at SemEval-2023 Task 9: An Ensemble Approach for Twitter Intimacy Analysis](https://aclanthology.org/2023.semeval-1.170) (Arampatzis et al., SemEval 2023)
ACL