@inproceedings{hussein-etal-2022-ngu,
title = "{NGU} {CNLP} at{WANLP} 2022 Shared Task: Propaganda Detection in {A}rabic",
author = "Hussein, Ahmed Samir and
Mohammad, Abu Bakr Soliman and
Ibrahim, Mohamed and
Afify, Laila Hesham and
El-Beltagy, Samhaa R.",
editor = "Bouamor, Houda and
Al-Khalifa, Hend and
Darwish, Kareem and
Rambow, Owen and
Bougares, Fethi and
Abdelali, Ahmed and
Tomeh, Nadi and
Khalifa, Salam and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.66",
doi = "10.18653/v1/2022.wanlp-1.66",
pages = "545--550",
abstract = "This paper presents the system developed by the NGU{\_}CNLP team for addressing the shared task on Propaganda Detection in Arabic at WANLP 2022. The team participated in the shared tasks{'} two sub-tasks which are: 1) Propaganda technique identification in text and 2) Propaganda technique span identification. In the first sub-task, the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques or to detect that no propaganda technique is being used in that piece of text. As such, this first sub-task is a multi-label classification problem with a pool of 18 possible labels. Subtask 2 extends sub-task 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. For task 1, a combination of a data augmentation strategy coupled with an enabled transformer-based model comprised our classification model. This classification model ranked first amongst the 14 systems participating in this subtask. For sub-task two, a transfer learning model was adopted. The system ranked third among the 3 different models that participated in this subtask.",
}
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<abstract>This paper presents the system developed by the NGU_CNLP team for addressing the shared task on Propaganda Detection in Arabic at WANLP 2022. The team participated in the shared tasks’ two sub-tasks which are: 1) Propaganda technique identification in text and 2) Propaganda technique span identification. In the first sub-task, the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques or to detect that no propaganda technique is being used in that piece of text. As such, this first sub-task is a multi-label classification problem with a pool of 18 possible labels. Subtask 2 extends sub-task 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. For task 1, a combination of a data augmentation strategy coupled with an enabled transformer-based model comprised our classification model. This classification model ranked first amongst the 14 systems participating in this subtask. For sub-task two, a transfer learning model was adopted. The system ranked third among the 3 different models that participated in this subtask.</abstract>
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%0 Conference Proceedings
%T NGU CNLP atWANLP 2022 Shared Task: Propaganda Detection in Arabic
%A Hussein, Ahmed Samir
%A Mohammad, Abu Bakr Soliman
%A Ibrahim, Mohamed
%A Afify, Laila Hesham
%A El-Beltagy, Samhaa R.
%Y Bouamor, Houda
%Y Al-Khalifa, Hend
%Y Darwish, Kareem
%Y Rambow, Owen
%Y Bougares, Fethi
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Khalifa, Salam
%Y Zaghouani, Wajdi
%S Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F hussein-etal-2022-ngu
%X This paper presents the system developed by the NGU_CNLP team for addressing the shared task on Propaganda Detection in Arabic at WANLP 2022. The team participated in the shared tasks’ two sub-tasks which are: 1) Propaganda technique identification in text and 2) Propaganda technique span identification. In the first sub-task, the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques or to detect that no propaganda technique is being used in that piece of text. As such, this first sub-task is a multi-label classification problem with a pool of 18 possible labels. Subtask 2 extends sub-task 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. For task 1, a combination of a data augmentation strategy coupled with an enabled transformer-based model comprised our classification model. This classification model ranked first amongst the 14 systems participating in this subtask. For sub-task two, a transfer learning model was adopted. The system ranked third among the 3 different models that participated in this subtask.
%R 10.18653/v1/2022.wanlp-1.66
%U https://aclanthology.org/2022.wanlp-1.66
%U https://doi.org/10.18653/v1/2022.wanlp-1.66
%P 545-550
Markdown (Informal)
[NGU CNLP atWANLP 2022 Shared Task: Propaganda Detection in Arabic](https://aclanthology.org/2022.wanlp-1.66) (Hussein et al., WANLP 2022)
ACL
- Ahmed Samir Hussein, Abu Bakr Soliman Mohammad, Mohamed Ibrahim, Laila Hesham Afify, and Samhaa R. El-Beltagy. 2022. NGU CNLP atWANLP 2022 Shared Task: Propaganda Detection in Arabic. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 545–550, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.