Abstract
In this paper, we present a high-performing model for Arabic stance detection on the STANCEEVAL2024 shared task part ofARABICNLP2024. Our model leverages ARABERTV1; a pre-trained Arabic language model, within a single-task learning framework. We fine-tuned the model on stance detection data for three specific topics: COVID19 vaccine, digital transformation, and women empowerment, extracted from the MAWQIF corpus. In terms of performance, our model achieves 73.30 macro-F1 score for women empowerment, 70.51 for digital transformation, and 64.55 for COVID-19 vaccine detection.- Anthology ID:
- 2024.arabicnlp-1.100
- Volume:
- Proceedings of The Second Arabic Natural Language Processing Conference
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Nizar Habash, Houda Bouamor, Ramy Eskander, Nadi Tomeh, Ibrahim Abu Farha, Ahmed Abdelali, Samia Touileb, Injy Hamed, Yaser Onaizan, Bashar Alhafni, Wissam Antoun, Salam Khalifa, Hatem Haddad, Imed Zitouni, Badr AlKhamissi, Rawan Almatham, Khalil Mrini
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 842–846
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.100
- DOI:
- Bibkey:
- Cite (ACL):
- Anas Melhem, Osama Hamed, and Thaer Sammar. 2024. TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 842–846, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT (Melhem et al., ArabicNLP-WS 2024)
- Copy Citation:
- PDF:
- https://aclanthology.org/2024.arabicnlp-1.100.pdf
Export citation
@inproceedings{melhem-etal-2024-tao,
title = "{TAO} at {S}tance{E}val2024 Shared Task: {A}rabic Stance Detection using {A}ra{BERT}",
author = "Melhem, Anas and
Hamed, Osama and
Sammar, Thaer",
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-1.100",
pages = "842--846",
abstract = "In this paper, we present a high-performing model for Arabic stance detection on the STANCEEVAL2024 shared task part ofARABICNLP2024. Our model leverages ARABERTV1; a pre-trained Arabic language model, within a single-task learning framework. We fine-tuned the model on stance detection data for three specific topics: COVID19 vaccine, digital transformation, and women empowerment, extracted from the MAWQIF corpus. In terms of performance, our model achieves 73.30 macro-F1 score for women empowerment, 70.51 for digital transformation, and 64.55 for COVID-19 vaccine detection.",
}
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%0 Conference Proceedings %T TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT %A Melhem, Anas %A Hamed, Osama %A Sammar, Thaer %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 melhem-etal-2024-tao %X In this paper, we present a high-performing model for Arabic stance detection on the STANCEEVAL2024 shared task part ofARABICNLP2024. Our model leverages ARABERTV1; a pre-trained Arabic language model, within a single-task learning framework. We fine-tuned the model on stance detection data for three specific topics: COVID19 vaccine, digital transformation, and women empowerment, extracted from the MAWQIF corpus. In terms of performance, our model achieves 73.30 macro-F1 score for women empowerment, 70.51 for digital transformation, and 64.55 for COVID-19 vaccine detection. %U https://aclanthology.org/2024.arabicnlp-1.100 %P 842-846
Markdown (Informal)
[TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT](https://aclanthology.org/2024.arabicnlp-1.100) (Melhem et al., ArabicNLP-WS 2024)
- TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT (Melhem et al., ArabicNLP-WS 2024)
ACL
- Anas Melhem, Osama Hamed, and Thaer Sammar. 2024. TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 842–846, Bangkok, Thailand. Association for Computational Linguistics.