@inproceedings{lichouri-etal-2024-dzstance,
title = "dz{S}tance at {S}tance{E}val2024: {A}rabic Stance Detection based on Sentence Transformers",
author = "Lichouri, Mohamed and
Lounnas, Khaled and
Rafik, Ouaras and
ABi, Mohamed and
Guechtouli, Anis",
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-1.91",
pages = "794--799",
abstract = "This study compares Term Frequency-Inverse Document Frequency (TF-IDF) features with Sentence Transformers for detecting writers{'} stances{---}favorable, opposing, or neutral{---}towards three significant topics: COVID-19 vaccine, digital transformation, and women empowerment. Through empirical evaluation, we demonstrate that Sentence Transformers outperform TF-IDF features across various experimental setups. Our team, dzStance, participated in a stance detection competition, achieving the 13th position (74.91{\%}) among 15 teams in Women Empowerment, 10th (73.43{\%}) in COVID Vaccine, and 12th (66.97{\%}) in Digital Transformation. Overall, our team{'}s performance ranked 13th (71.77{\%}) among all participants. Notably, our approach achieved promising F1-scores, highlighting its effectiveness in identifying writers{'} stances on diverse topics. These results underscore the potential of Sentence Transformers to enhance stance detection models for addressing critical societal issues.",
}
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<abstract>This study compares Term Frequency-Inverse Document Frequency (TF-IDF) features with Sentence Transformers for detecting writers’ stances—favorable, opposing, or neutral—towards three significant topics: COVID-19 vaccine, digital transformation, and women empowerment. Through empirical evaluation, we demonstrate that Sentence Transformers outperform TF-IDF features across various experimental setups. Our team, dzStance, participated in a stance detection competition, achieving the 13th position (74.91%) among 15 teams in Women Empowerment, 10th (73.43%) in COVID Vaccine, and 12th (66.97%) in Digital Transformation. Overall, our team’s performance ranked 13th (71.77%) among all participants. Notably, our approach achieved promising F1-scores, highlighting its effectiveness in identifying writers’ stances on diverse topics. These results underscore the potential of Sentence Transformers to enhance stance detection models for addressing critical societal issues.</abstract>
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%0 Conference Proceedings
%T dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers
%A Lichouri, Mohamed
%A Lounnas, Khaled
%A Rafik, Ouaras
%A ABi, Mohamed
%A Guechtouli, Anis
%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 lichouri-etal-2024-dzstance
%X This study compares Term Frequency-Inverse Document Frequency (TF-IDF) features with Sentence Transformers for detecting writers’ stances—favorable, opposing, or neutral—towards three significant topics: COVID-19 vaccine, digital transformation, and women empowerment. Through empirical evaluation, we demonstrate that Sentence Transformers outperform TF-IDF features across various experimental setups. Our team, dzStance, participated in a stance detection competition, achieving the 13th position (74.91%) among 15 teams in Women Empowerment, 10th (73.43%) in COVID Vaccine, and 12th (66.97%) in Digital Transformation. Overall, our team’s performance ranked 13th (71.77%) among all participants. Notably, our approach achieved promising F1-scores, highlighting its effectiveness in identifying writers’ stances on diverse topics. These results underscore the potential of Sentence Transformers to enhance stance detection models for addressing critical societal issues.
%U https://aclanthology.org/2024.arabicnlp-1.91
%P 794-799
Markdown (Informal)
[dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers](https://aclanthology.org/2024.arabicnlp-1.91) (Lichouri et al., ArabicNLP-WS 2024)
ACL