DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers

TL;DR

This paper introduces DarijaBanking, a new multilingual dataset for Moroccan Arabic banking intent detection, and proposes BERTouch, a BERT-based model that achieves high accuracy, advancing NLP tools for Darija in banking applications.

Contribution

The paper presents DarijaBanking, a novel dataset for Moroccan Arabic, and introduces BERTouch, a specialized BERT-based model that significantly improves intent classification performance.

Findings

BERTouch achieved 0.98 F1-score for Darija.

High accuracy demonstrated across multiple languages.

Outperformed GPT-4 and other state-of-the-art models.

Abstract

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.