Augmentation and Classification of Requests in Moroccan Dialect to Improve Quality of Public Service: A Comparative Study of Algorithms

Hajar Zaidani, Rim Koulali, Abderrahim Maizate () and Mohamed Ouzzif
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Hajar Zaidani: RITM Laboratory, Higher School of Technology Casablanca, CED ENSEM, Hassan II University Casablanca, Casablanca 20430, Morocco
Rim Koulali: LIS Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Casablanca 20100, Morocco
Abderrahim Maizate: RITM Laboratory, Higher School of Technology Casablanca, CED ENSEM, Hassan II University Casablanca, Casablanca 20430, Morocco
Mohamed Ouzzif: C3S Laboratory, Higher School of Technology Casablanca, CED ENSEM, Hassan II University Casablanca, Casablanca 20430, Morocco

Future Internet, 2025, vol. 17, issue 4, 1-25

Abstract: Moroccan Law 55.19 aims to streamline administrative procedures, fostering trust between citizens and public administrations. To implement this law effectively and enhance public service quality, it is essential to use the Moroccan dialect to involve a wide range of people by leveraging Natural Language Processing (NLP) techniques customized to its specific linguistic characteristics. It is worth noting that the Moroccan dialect presents a unique linguistic landscape, marked by the coexistence of multiple texts. Though it has emerged as the preferred medium of communication on social media, reaching wide audiences, its perceived difficulty of comprehension remains unaddressed. This article introduces a new approach to addressing these challenges. First, we compiled and processed a dataset of Moroccan dialect requests for public administration documents, employing a new augmentation technique to enhance its size and diversity. Second, we conducted text classification experiments using various machine learning algorithms, ranging from traditional methods to advanced large language models (LLMs), to categorize the requests into three classes. The results indicate promising outcomes, with an accuracy of more than 80% for LLMs. Finally, we propose a chatbot system architecture for deploying the most efficient classification algorithm. This solution also contains a voice assistant system that can contribute to the social inclusion of illiterate people. The article concludes by outlining potential avenues for future research.

Keywords: text classification; natural language processing; Moroccan dialect; supervised machine learning; deep learning; LLMs (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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