Text Correction for Modern Standard Arabic

Abstract

Arabic poses a unique challenge for Natural Language Processing tasks due to its morphological complexity, rich vocabulary, and syntactic flexibility. Mistakes written in Arabic are common even among fluent speakers, and slight mistakes can obstruct a word's meaning. Our project investigates Large Language Models (LLMs)’ capabilities for detecting and automatically correcting syntax and semantic errors in Arabic text. Our project includes an overview of Qatar Arabic Language Bank (QALB), a shared task on automatic correction of Arabic text, which focuses on correcting errors in Arabic text produced by native speakers. We used the QALB dataset for training and evaluation, achieving a WER score of 0.2203 and a GLEU of 0.5956.