🤖 AI Summary
This work addresses the scarcity of high-quality linguistic resources for Algerian Arabic (Darja)—a low-resource dialect hindered by non-standardized spelling, frequent code-switching with French, and dual-script usage—which impedes the development of intelligent dialogue systems. To overcome these challenges, we propose a hybrid conversational agent architecture tailored for Darja, integrating dedicated natural language understanding with retrieval-augmented generation (RAG) to support structured service workflows and dynamic responses grounded in enterprise knowledge bases. We evaluate three approaches: sparse features, traditional machine learning, and a fine-tuned Transformer model (DziriBERT). Notably, this study presents the first deployment of a dialect-level RAG dialogue system in a real-world business setting. The fine-tuned DziriBERT achieves state-of-the-art performance in intent recognition, significantly outperforming baseline models and demonstrating robustness and scalability in handling spelling variations and rare intents.
📝 Abstract
The rapid digitalization of customer service has intensified the demand for conversational agents capable of providing accurate and natural interactions. In the Algerian context, this is complicated by the linguistic complexity of Darja, a dialect characterized by non-standardized orthography, extensive code-switching with French, and the simultaneous use of Arabic and Latin (Arabizi) scripts. This paper introduces DziriBOT, a hybrid intelligent conversational agent specifically engineered to overcome these challenges. We propose a multi-layered architecture that integrates specialized Natural Language Understanding (NLU) with Retrieval-Augmented Generation (RAG), allowing for both structured service flows and dynamic, knowledge-intensive responses grounded in curated enterprise documentation. To address the low-resource nature of Darja, we systematically evaluate three distinct approaches: a sparse-feature Rasa pipeline, classical machine learning baselines, and transformer-based fine-tuning. Our experimental results demonstrate that the fine-tuned DziriBERT model achieves state-of-the-art performance. These results significantly outperform traditional baselines, particularly in handling orthographic noise and rare intents. Ultimately, DziriBOT provides a robust, scalable solution that bridges the gap between formal language models and the linguistic realities of Algerian users, offering a blueprint for dialect-aware automation in the regional market.