AraProp at WANLP 2022 Shared Task: Leveraging Pre-Trained Language Models for Arabic Propaganda Detection

Abstract

This paper presents the approach taken for the shared task on Propaganda Detection in Arabic at the Seventh Arabic Natural Language Processing Workshop (WANLP 2022). We participated in Sub-task 1 where the text of a tweet is provided, and the goal is to identify the different propaganda techniques used in it. This problem belongs to multi-label classification. For our solution, we approached leveraging different transformer based pre-trained language models with fine-tuning to solve this problem. We found that MARBERTv2 outperforms in terms of performance where F1-macro is 0.08175 and F1-micro is 0.61116 compared to other language models that we considered. Our method achieved rank 4 in the testing phase of the challenge.