Year: 2024
Type: conference-paper
Abstract: The challenges of automated transliteration and codeswitching-detection in JudeoArabic texts are addressed.We introduce two novel machinelearning models, one focused on transliterating JudeoArabic into Arabic, and another aimed at identifying nonArabic words, predominantly Hebrew and Aramaic.Unlike prior work, our models are based on a bilingual ArabicHebrew language model, providing a unique advantage in capturing shared linguistic nuances.Evaluation results show that our mod els outperform prior solutions for the same tasks.As a practical contribution, we present a com prehensive pipeline capable of taking Judeo Arabic text, identifying nonArabic words, and then transliterating the Arabic portions into Ara bic script.This work not only advances the state of the art but also offers a valuable toolset for making JudeoArabic texts more accessible to a broader Arabicspeaking audience and more amenable to modern language tools.
Institutions: Tel Aviv University, Reichman University
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: European Commission
Award: G1861717563
OpenAlex ID: W7126460603