Year: 2025
Type: conference-paper
Abstract: Video-to-text and text-to-video retrieval are dominated by English benchmarks (e.g.DiDeMo, MSR-VTT) and recent multilingual corpora (e.g.RUDDER), yet Arabic remains underserved, lacking localized evaluation metrics.We introduce a three-stage framework, AUTOARABIC, utilizing state-of-the-art large language models (LLMs) to translate non-Arabic benchmarks into Modern Standard Arabic, reducing the manual revision required by nearly fourfold.The framework incorporates an error detection module that automatically flags potential translation errors with 97% accuracy.Applying the framework to DiDeMo, a video retrieval benchmark produces DiDeMo-AR, an Arabic variant with 40,144 fluent Arabic descriptions.An analysis of the translation errors is provided and organized into an insightful taxonomy to guide future Arabic localization efforts.We train a CLIP-style baseline with identical hyperparameters on the Arabic and English variants of the benchmark, finding a moderate performance gap (∆ ≈ 3 pp at Recall@1), indicating that Arabic localization preserves benchmark difficulty.We evaluate three postediting budgets (zero/ flagged-only/ full) and find that performance improves monotonically with more post-editing, while the raw LLM output (zero-budget) remains usable.To ensure reproducibility to other languages, we made the code available at https://github. com/Tahaalshatiri/AutoArabic.
Authors: Mohamed Eltahir, Osamah Sarraj, Abdulrahman M. Alfrihidi, Taha Alshatiri, Mohammed Khurd, Mohammed Bremoo, Tanveer Hussain
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4416037683