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It is examined how Arabic’s key linguistic features—non‑concatenative morphology, broken plurals, diacritic ambiguity, and root‑based semantics—affect enrichment outcomes and assess their effects across different enrichment techniques.
A bidirectional Mamba encoder pretrained via masked language modeling on a corpus combining Arabic Wikipedia and CulturaX text is introduced, trained end-to-end on four consumer-grade NVIDIA RTX 2080Ti GPUs (11GB) over approximately ten days.
This paper proposes a general optimization framework that combines a vocabulary pruning method with a targeted fine-tuning protocol for MNMT models, and reduces the vocabulary size from over 128,000 to approximately 10,000 tokens, enabling a 60% memory saving without any loss in performance.
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