Mohamed Hannani

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2024

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Exploring the Potential of Large Language Models in Adaptive Machine Translation for Generic Text and Subtitles
Abdelhadi Soudi | Mohamed Hannani | Kristof Van Laerhoven | Eleftherios Avramidis
Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC) @ LREC-COLING 2024

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Assessing the Performance of ChatGPT-4, Fine-tuned BERT and Traditional ML Models on Moroccan Arabic Sentiment Analysis
Mohamed Hannani | Abdelhadi Soudi | Kristof Van Laerhoven
Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities

Large Language Models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks across different languages. However, their performance in low-resource languages and dialects, such as Moroccan Arabic (MA), requires further investigation. This study evaluates the performance of ChatGPT-4, different fine-tuned BERT models, FastText as text representation, and traditional machine learning models on MA sentiment analysis. Experiments were done on two open source MA datasets: an X(Twitter) Moroccan Arabic corpus (MAC) and a Moroccan Arabic YouTube corpus (MYC) datasets to assess their capabilities on sentiment text classification. We compare the performance of fully fine-tuned and pre-trained Arabic BERT-based models with ChatGPT-4 in zero-shot settings.
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