Abstract
Arabic Sign Language (ArSL) is a language used by the deaf community across Arab countries, but the lack of familiarity with ArSL among the hearing population often leads to social isolation for deaf individuals. The structural differences between ArSL and spoken Arabic pose significant challenges for machine translation. In this study, we enhance Arabic to ArSL gloss translation by employing data augmentation techniques, expanding the dataset from 600 to over 23,328 samples using sequence-to-sequence transformer models. Our approach achieved a substantial performance improvement, increasing the BLEU score from 11.1% in the baseline model to 52.72% on original test set. The best model achieved a BLEU score of 85.17% on augmented data test, underscoring the effectiveness of data augmentation in enhancing ArSL translation quality.
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