@inproceedings{tuck-etal-2023-detectiveredasers,
title = "{D}etective{R}edasers at {A}r{AIE}val Shared Task: Leveraging Transformer Ensembles for {A}rabic Deception Detection",
author = "Tuck, Bryan and
Qachfar, Fatima Zahra and
Boumber, Dainis and
Verma, Rakesh",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.45",
doi = "10.18653/v1/2023.arabicnlp-1.45",
pages = "494--501",
abstract = "This paper outlines a methodology aimed at combating disinformation in Arabic social media, a strategy that secured a first-place finish in tasks 2A and 2B at the ArAIEval shared task during the ArabicNLP 2023 conference. Our team, DetectiveRedasers, developed a hyperparameter-optimized pipeline centered around singular BERT-based models for the Arabic language, enhanced by a soft-voting ensemble strategy. Subsequent evaluation on the test dataset reveals that ensembles, although generally resilient, do not always outperform individual models. The primary contributions of this paper are its multifaceted strategy, which led to winning solutions for both binary (2A) and multiclass (2B) disinformation classification tasks.",
}
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%0 Conference Proceedings
%T DetectiveRedasers at ArAIEval Shared Task: Leveraging Transformer Ensembles for Arabic Deception Detection
%A Tuck, Bryan
%A Qachfar, Fatima Zahra
%A Boumber, Dainis
%A Verma, Rakesh
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F tuck-etal-2023-detectiveredasers
%X This paper outlines a methodology aimed at combating disinformation in Arabic social media, a strategy that secured a first-place finish in tasks 2A and 2B at the ArAIEval shared task during the ArabicNLP 2023 conference. Our team, DetectiveRedasers, developed a hyperparameter-optimized pipeline centered around singular BERT-based models for the Arabic language, enhanced by a soft-voting ensemble strategy. Subsequent evaluation on the test dataset reveals that ensembles, although generally resilient, do not always outperform individual models. The primary contributions of this paper are its multifaceted strategy, which led to winning solutions for both binary (2A) and multiclass (2B) disinformation classification tasks.
%R 10.18653/v1/2023.arabicnlp-1.45
%U https://aclanthology.org/2023.arabicnlp-1.45
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.45
%P 494-501
Markdown (Informal)
[DetectiveRedasers at ArAIEval Shared Task: Leveraging Transformer Ensembles for Arabic Deception Detection](https://aclanthology.org/2023.arabicnlp-1.45) (Tuck et al., ArabicNLP-WS 2023)
ACL