rematchka at ArAIEval Shared Task: Prefix-Tuning & Prompt-tuning for Improved Detection of Propaganda and Disinformation in Arabic Social Media Content

Résumé

The rise of propaganda and disinformation in the digital age has necessitated the development of effective detection methods to combat the spread of deceptive information.In this paper, we present our approach proposed for the ArAIEval shared task: propaganda and disinformation detection in Arabic text.Our system utilized different pre-trained BERT based models, that make use of prompt-learning based on knowledgeable expansion and prefix-tuning.The proposed approach secured third place in subtask-1A with a 0.7555 F1-micro score, and second place in subtask-1B with a 0.5658 F1micro score.However, for subtask-2A & 2B, the proposed system achieved fourth place with an F1-micro score of 0.9040, and 0.8219 respectively.Our findings suggest that prompttuning-based & prefix-tuning based models performed better than conventional fine-tuning.Furthermore, using loss-aware class imbalance, improved performance.

Citer ce document

Accès au document

Ce document est hébergé sur son dépôt institutionnel d'origine.

Auteur(s)

Statistiques

Consultations : 2

Téléchargements : 0