AlexUNLP-BH at StanceEval2024: Multiple Contrastive Losses Ensemble Strategy with Multi-Task Learning For Stance Detection in Arabic
Year: 2024
Type: conference-paper
Abstract: Stance detection, an evolving task in natural language processing, involves understanding a writer's perspective on certain topics by analyzing his written text and interactions online, especially on social media platforms.In this paper, we outline our submission to the StanceEval 2024 task, leveraging the Mawqif dataset featured in The Second Arabic Natural Language Processing Conference.Our task is to detect writers' stances (Favor, Against, or None) towards three selected topics (COVID-19 vaccine, digital transformation, and women empowerment).We present our approach primarily relying on a contrastive loss ensemble strategy.Our proposed approach achieved an F1 score of 0.8438 and ranked first in the stanceEval 2024 task.The code and checkpoints are available at https://github.com/MBadran2000/Mawqif.git
Authors: Mohamed Badran, Mo’men Hamdy, Marwan Torki, Nagwa El-Makky
Institution: Alexandria University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402671313