Nullpointer at ArAIEval Shared Task: Arabic Propagandist Technique Detection with Token-to-Word Mapping in Sequence Tagging
Year: 2024
Type: conference-paper
Abstract: This paper investigates the optimization of propaganda technique detection in Arabic text, including tweets & news paragraphs, from ArAIEval shared task 1.Our approach involves fine-tuning the AraBERT v2 model with a neural network classifier for sequence tagging.Experimental results show relying on the first token of the word for technique prediction produces the best performance.In addition, incorporating genre information as a feature further enhances the model's performance.Our system achieved a score of 25.41, placing us 4 th on the leaderboard.Subsequent postsubmission improvements further raised our score to 26.68.
Authors: Abrar Abir, Kemal Oflazer
Institutions: Carnegie Mellon University Qatar, Carnegie Mellon University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4402671357