Year: 2025
Type: conference-paper
Abstract: Processing North African Arabic dialects presents significant challenges due to high lexical variability, frequent code-switching with French, and the use of both Arabic and Latin scripts.We address this with a phonemebased normalization strategy (Toughrai et al., 2025) that maps Arabic and French text into a simplified representation (Arabic rendered in Latin script), reflecting native reading patterns.Using this method, we pretrain BERTbased models on normalized Modern Standard Arabic and French only and evaluate them on Named Entity Recognition (NER) and text classification.Experiments show that normalized standard-language corpora yield competitive performance on North African dialect tasks; in zero-shot NER, Ar_20k surpasses dialectpretrained baselines.Normalization improves vocabulary alignment, indicating that normalized standard corpora can suffice for developing dialect-supportive language models in lowresource contexts.
Authors: Yassine Toughrai, Kamel Smaïli, David Langlois
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4416030414