  Navigating the complexities of language diversity is a central challenge in
developing robust natural language processing systems, especially in
specialized domains like banking. The Moroccan Dialect (Darija) serves as the
common language that blends cultural complexities, historical impacts, and
regional differences. The complexities of Darija present a special set of
challenges for language models, as it differs from Modern Standard Arabic with
strong influence from French, Spanish, and Tamazight, it requires a specific
approach for effective communication. To tackle these challenges, this paper
introduces \textbf{DarijaBanking}, a novel Darija dataset aimed at enhancing
intent classification in the banking domain, addressing the critical need for
automatic banking systems (e.g., chatbots) that communicate in the native
language of Moroccan clients. DarijaBanking comprises over 1,800 parallel
high-quality queries in Darija, Modern Standard Arabic (MSA), English, and
French, organized into 24 intent classes. We experimented with various intent
classification methods, including full fine-tuning of monolingual and
multilingual models, zero-shot learning, retrieval-based approaches, and Large
Language Model prompting. One of the main contributions of this work is
BERTouch, our BERT-based language model for intent classification in Darija.
BERTouch achieved F1-scores of 0.98 for Darija and 0.96 for MSA on
DarijaBanking, outperforming the state-of-the-art alternatives including GPT-4
showcasing its effectiveness in the targeted application.
