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REGLAT at AraGenEval shared task: Morphology-Aware AraBERT for Detecting Arabic AI-Generated Text

Year: 2025

Type: conference-paper

Abstract: The emergence of large language models has underscored the need for effective methodologies to differentiate between machinegenerated and human-authored Arabic text.This study introduces a transformer-based classification system designed for the AraGenEval shared task focused on detecting AI-generated Arabic text.The proposed approach employs AraBERTv2 as the backbone architecture, augmented with a comprehensive preprocessing pipeline that addresses Arabic-specific orthographic variations through systematic diacritic removal and character normalization.Experimental results indicate that this preprocessingenhanced approach achieves a weighted F1 score of 0.63 on the test dataset, demonstrating particularly strong performance in modern standard Arabic texts.The results suggest that morphological normalization is crucial for the detection of AI-generated Arabic text, surpassing the significance of similar preprocessing techniques in other languages.

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.18653/v1/2025.arabicnlp-sharedtasks.16

Authors: Mariam Labib, Nsrin Ashraf, Mohammed Aldawsari, Hamada Nayel

Institutions: Mansoura University, ElSewedy University of Technology, Benha University, Prince Sattam Bin Abdulaziz University

Language: English

Topic: Authorship Attribution and Profiling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4416037795

FWCI: 0.953

Cites:

Cited by: 1

Related to: 10