Ara-HOPE: A Human-Centric Framework for Evaluating Arabic Dialect Translation
Published:Dec 25, 2025 21:29
•1 min read
•ArXiv
Analysis
This paper addresses a critical need in machine translation: the accurate evaluation of dialectal Arabic translation. Existing metrics often fail to capture the nuances of dialect-specific errors. Ara-HOPE provides a structured, human-centric framework (error taxonomy and annotation protocol) to overcome this limitation. The comparative evaluation of different MT systems using Ara-HOPE demonstrates its effectiveness in highlighting performance differences and identifying persistent challenges in DA-MSA translation. This is a valuable contribution to the field, offering a more reliable method for assessing and improving dialect-aware MT systems.
Key Takeaways
- •Introduces Ara-HOPE, a human-centric framework for evaluating Dialectal Arabic to Modern Standard Arabic translation.
- •Provides a five-category error taxonomy and a decision-tree annotation protocol.
- •Effectively highlights performance differences between MT systems.
- •Identifies dialect-specific terminology and semantic preservation as key challenges.
Reference
“The results show that dialect-specific terminology and semantic preservation remain the most persistent challenges in DA-MSA translation.”