dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers
<|reference_start|>dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers: 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.<|reference_end|>
@article{lichouri2024dzstance,
    title={dzStance at StanceEval2024: Arabic Stance Detection based on Sentence
  Transformers},
    author={Mohamed Lichouri, Khaled Lounnas, Khelil Rafik Ouaras, Mohamed Abi,
  Anis Guechtouli},
    journal={arXiv preprint arXiv:2407.13603},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13603},
    primaryClass={cs.CL}
}