Thaer Sammar


2024

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TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT
Anas Melhem | Osama Hamed | Thaer Sammar
Proceedings of The Second Arabic Natural Language Processing Conference

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.