@inproceedings{mangalvedhekar-etal-2023-mavericks,
title = "Mavericks at {A}r{AIE}val Shared Task: Towards a Safer Digital Space - Transformer Ensemble Models Tackling Deception and Persuasion",
author = "Mangalvedhekar, Sudeep and
Deshpande, Kshitij and
Patwardhan, Yash and
Deshpande, Vedant and
Murumkar, Ravindra",
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-1.48",
doi = "10.18653/v1/2023.arabicnlp-1.48",
pages = "513--518",
abstract = "In this paper, we highlight our approach for the {``}Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023{''}. We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.",
}
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<abstract>In this paper, we highlight our approach for the “Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023”. We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.</abstract>
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%0 Conference Proceedings
%T Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space - Transformer Ensemble Models Tackling Deception and Persuasion
%A Mangalvedhekar, Sudeep
%A Deshpande, Kshitij
%A Patwardhan, Yash
%A Deshpande, Vedant
%A Murumkar, Ravindra
%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 mangalvedhekar-etal-2023-mavericks
%X In this paper, we highlight our approach for the “Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023”. We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distortion of authentic information. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We experiment with several transformer-based models that were pre-trained on the Arabic language. We fine-tune these state-of-the-art models on the provided dataset. Ensembling is employed to enhance the performance of the systems. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.
%R 10.18653/v1/2023.arabicnlp-1.48
%U https://aclanthology.org/2023.arabicnlp-1.48
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.48
%P 513-518
Markdown (Informal)
[Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space - Transformer Ensemble Models Tackling Deception and Persuasion](https://aclanthology.org/2023.arabicnlp-1.48) (Mangalvedhekar et al., ArabicNLP-WS 2023)
ACL