dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers
This is an incremental improvement for Arabic NLP researchers, addressing stance detection on societal issues.
This study tackled stance detection in Arabic text on topics like COVID-19 vaccine and women empowerment, finding that Sentence Transformers outperformed TF-IDF features, with competition rankings ranging from 10th to 13th and overall performance at 71.77%.
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.