MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation

Generative AI & LLMs

Published: arXiv: 2508.13130v1

Authors

Kareem Elozeiri Mervat Abassy Preslav Nakov Yuxia Wang

Abstract

Commonsense validation evaluates whether a sentence aligns with everyday human understanding, a critical capability for developing robust natural language understanding systems. While substantial progress has been made in English, the task remains underexplored in Arabic, particularly given its rich linguistic diversity. Existing Arabic resources have primarily focused on Modern Standard Arabic (MSA), leaving regional dialects underrepresented despite their prevalence in spoken contexts. To bridge this gap, we present two key contributions: (i) we introduce MuDRiC, an extended Arabic commonsense dataset incorporating multiple dialects, and (ii) a novel method adapting Graph Convolutional Networks (GCNs) to Arabic commonsense reasoning, which enhances semantic relationship modeling for improved commonsense validation. Our experimental results demonstrate that this approach achieves superior performance in Arabic commonsense validation. Our work enhances Arabic natural language understanding by providing both a foundational dataset and a novel method for handling its complex variations. To the best of our knowledge, we release the first Arabic multi-dialect commonsense reasoning dataset.

Paper Summary

Problem

The main problem addressed by this research is the lack of a common sense dataset for Arabic dialects, despite their prevalence in spoken contexts and formal settings. Most existing datasets focus on Modern Standard Arabic (MSA), neglecting the rich diversity of Arabic dialects. This gap limits the applicability of models trained on MSA to real-world dialectal content.

Key Innovation

The key innovation is the introduction of MuDRiC, a multi-dialect common sense benchmark that incorporates four major Arabic dialects: Egyptian, Gulf, Levantine, and Moroccan. Additionally, the research presents a novel approach adapting Graph Convolutional Networks (GCNs) to Arabic commonsense reasoning, which enhances semantic relationship modeling for improved commonsense validation.

Practical Impact

The practical impact of this research is the provision of a foundational dataset and a novel methodology for handling the complex variations in Arabic dialects. This will enhance Arabic natural language understanding by enabling AI systems to interpret and generate text in ways that align with human intuition. The dataset and framework can be applied in various real-world scenarios, such as chatbots, voice assistants, or social media platforms.

Analogy / Intuitive Explanation

Imagine trying to understand a conversation between two people speaking different dialects of Arabic. You might struggle to pick up on the nuances of each dialect, even if you're familiar with one of them. That's what's happening when AI systems are trained only on MSA and then applied to real-world dialectal content. The MuDRiC dataset is like a Rosetta Stone for Arabic dialects, providing a common language understanding framework that can help bridge the gap between different dialects.

Paper Information

Categories:

cs.CL

Published Date:

arXiv ID:

2508.13130v1

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