[2603.19260] HATL: Hierarchical Adaptive-Transfer Learning Framework for Sign Language Machine Translation

Computer Science > Computation and Language arXiv:2603.19260 (cs) [Submitted on 26 Feb 2026] Title:HATL: Hierarchical Adaptive-Transfer Learning Framework for Sign Language Machine Translation Authors:Nada Shahin, Leila Ismail View a PDF of the paper titled HATL: Hierarchical Adaptive-Transfer Learning Framework for Sign Language Machine Translation, by Nada Shahin and Leila Ismail View PDF HTML (experimental) Abstract:Sign Language Machine Translation (SLMT) aims to bridge communication between Deaf and hearing individuals. However, its progress is constrained by scarce datasets, limited signer diversity, and large domain gaps between sign motion patterns and pretrained representations. Existing transfer learning approaches in SLMT are static and often lead to overfitting. These challenges call for the development of an adaptive framework that preserves pretrained structure while remaining robust across linguistic and signing variations. To fill this void, we propose a Hierarchical Adaptive Transfer Learning (HATL) framework, where pretrained layers are progressively and dynamically unfrozen based on training performance behavior. HATL combines dynamic unfreezing, layer-wise learning rate decay, and stability mechanisms to preserve generic representations while adapting to sign characteristics. We evaluate HATL on Sign2Text and Sign2Gloss2Text translation tasks using a pretrained ST-GCN++ backbone for feature extraction and the Transformer and an adaptive transformer (ADA...