MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation
This work addresses the problem of underrepresentation of regional dialects in Arabic natural language understanding, providing a foundational dataset and method for researchers and developers, though it is incremental in extending existing techniques to a new linguistic context.
The authors tackled the lack of Arabic commonsense validation resources by introducing MuDRiC, a multi-dialect dataset, and a GCN-based method, achieving superior performance in Arabic commonsense validation.
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.