This study evaluated the adversarial robustness of five state-of-the-art Arabic Language Models against various Arabic adversarial attacks at character, word, and sentence levels. It found that diacritic insertion could reduce model accuracy by up to 92%, while manipulating Arabic conjunctions led to a 58% accuracy degradation, and paraphrasing reduced performance by an average of 76%. While adversarial training improved overall resilience, particularly for MARBERT and AraBERT, challenges against character-level noise persist. Why it matters: These findings are crucial for understanding and mitigating security vulnerabilities in Arabic AI, guiding the development of more robust and safe Arabic NLP systems.
The paper introduces AlcLaM, an Arabic dialectal language model trained on 3.4M sentences from social media. AlcLaM expands the vocabulary and retrains a BERT-based model, using only 13GB of dialectal text. Despite the smaller training data, AlcLaM outperforms models like CAMeL, MARBERT, and ArBERT on various Arabic NLP tasks. Why it matters: AlcLaM offers a more efficient and accurate approach to Arabic NLP by focusing on dialectal Arabic, which is often underrepresented in existing models.
A study focused on developing a model for spam and sentiment detection in Arabic tweets, specifically targeting customer feedback for Saudi Telecom Company (STC). Researchers trained the MARBERT model using a dataset of 24,513 Arabic tweets, which included various sentiment categories like positive, negative, neutral, sarcasm, and indeterminate. The primary objective was to analyze tweet sentiments to enhance STC's customer service, with the proposed scheme demonstrating promising accuracy compared to existing techniques. Why it matters: This research contributes to Arabic Natural Language Processing (NLP) by providing a practical application for sentiment analysis in customer service within the Middle East, addressing a recognized gap in Arabic AI research.