dziribot: rag based intelligent conversational agent for algerian arabic dialect

TL;DR

DziriBOT is a novel hybrid conversational agent tailored for Algerian Darja, integrating RAG and specialized NLU to handle linguistic complexities, outperforming traditional models in low-resource dialect scenarios.

Contribution

This paper introduces DziriBOT, a dialect-aware conversational agent using a multi-layered RAG architecture and fine-tuned transformer models for Algerian Arabic dialect.

Findings

Fine-tuned DziriBERT achieves state-of-the-art performance.

Outperforms traditional baselines in handling orthographic noise.

Provides a scalable solution for dialect-aware automation.

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.