@inproceedings{badran-etal-2024-alexunlp,
title = "{A}lex{UNLP}-{BH} at {S}tance{E}val2024: Multiple Contrastive Losses Ensemble Strategy with Multi-Task Learning For Stance Detection in {A}rabic",
author = "Badran, Mohamed and
Hamdy, Mo{'}men and
Torki, Marwan and
El-Makky, Nagwa",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.96",
pages = "823--827",
abstract = "Stance detection, an evolving task in natural language processing, involves understanding a writer{'}s perspective on certain topics by analyzing his written text and interactions online, especially on social media platforms. In this paper, we outline our submission to the StanceEval task, leveraging the Mawqif dataset featured in The Second Arabic Natural Language Processing Conference. Our task is to detect writers{'} stances (Favor, Against, or None) towards three selected topics (COVID-19 vaccine, digital transformation, and women empowerment). We present our approach primarily relying on a contrastive loss ensemble strategy. Our proposed approach achieved an F1-score of 0.8438 and ranked first in the stanceEval 2024 task. The code and checkpoints are availableat https://github.com/MBadran2000/Mawqif.git",
}
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%0 Conference Proceedings
%T AlexUNLP-BH at StanceEval2024: Multiple Contrastive Losses Ensemble Strategy with Multi-Task Learning For Stance Detection in Arabic
%A Badran, Mohamed
%A Hamdy, Mo’men
%A Torki, Marwan
%A El-Makky, Nagwa
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F badran-etal-2024-alexunlp
%X Stance detection, an evolving task in natural language processing, involves understanding a writer’s perspective on certain topics by analyzing his written text and interactions online, especially on social media platforms. In this paper, we outline our submission to the StanceEval task, leveraging the Mawqif dataset featured in The Second Arabic Natural Language Processing Conference. Our task is to detect writers’ stances (Favor, Against, or None) towards three selected topics (COVID-19 vaccine, digital transformation, and women empowerment). We present our approach primarily relying on a contrastive loss ensemble strategy. Our proposed approach achieved an F1-score of 0.8438 and ranked first in the stanceEval 2024 task. The code and checkpoints are availableat https://github.com/MBadran2000/Mawqif.git
%U https://aclanthology.org/2024.arabicnlp-1.96
%P 823-827
Markdown (Informal)
[AlexUNLP-BH at StanceEval2024: Multiple Contrastive Losses Ensemble Strategy with Multi-Task Learning For Stance Detection in Arabic](https://aclanthology.org/2024.arabicnlp-1.96) (Badran et al., ArabicNLP-WS 2024)
ACL