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dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers

arXiv 2407.13603 ↗First posted 18 Jul 2024cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 18 Jul 2024.

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3 of 10 references.

References · 3

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2020Stance Detection on Social Media: State of the Art and Trendscited by 95
  3. 2023USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identificationcited by 3

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