TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT

Anas Melhem; Osama Hamed; Thaer Sammar

doi:10.18653/v1/2024.arabicnlp-1.100

TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT

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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:: SIGARAB
Publisher:: Association for Computational Linguistics
Note:
Pages:: 842–846
Language:
URL:: https://aclanthology.org/2024.arabicnlp-1.100/
DOI:: 10.18653/v1/2024.arabicnlp-1.100
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 2024)
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PDF:: https://aclanthology.org/2024.arabicnlp-1.100.pdf

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@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/",
    doi = "10.18653/v1/2024.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.
%R 10.18653/v1/2024.arabicnlp-1.100
%U https://aclanthology.org/2024.arabicnlp-1.100/
%U https://doi.org/10.18653/v1/2024.arabicnlp-1.100
%P 842-846

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Markdown (Informal)

[TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT](https://aclanthology.org/2024.arabicnlp-1.100/) (Melhem et al., ArabicNLP 2024)

TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT (Melhem et al., ArabicNLP 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.