HATL: Hierarchical Adaptive-Transfer Learning Framework for Sign Language Machine Translation
arXiv:2603.19260v1 Announce Type: cross 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 trans
arXiv:2603.19260v1 Announce Type: cross 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 (ADAT)for translation. To ensure robust multilingual generalization, we evaluate the proposed approach across three datasets: RWTH-PHOENIXWeather-2014 (PHOENIX14T), Isharah, and MedASL. Experimental results show that HATL consistently outperforms traditional transfer learning approaches across tasks and models, with ADAT achieving BLEU-4 improvements of 15.0% on PHOENIX14T and Isharah and 37.6% on MedASL.
Executive Summary
The article proposes a Hierarchical Adaptive-Transfer Learning (HATL) framework to address the challenges in Sign Language Machine Translation (SLMT), including scarce datasets, limited signer diversity, and large domain gaps. HATL progressively unfreezes pretrained layers based on training performance behavior, combining dynamic unfreezing, layer-wise learning rate decay, and stability mechanisms. Experimental results demonstrate that HATL outperforms traditional transfer learning approaches on three datasets, with significant improvements in BLEU-4 scores. The proposed framework has the potential to bridge communication between Deaf and hearing individuals, enhancing accessibility and inclusivity. By adapting to sign characteristics and preserving generic representations, HATL shows promise in addressing the complexities of SLMT.
Key Points
- ▸ HATL framework combines dynamic unfreezing, layer-wise learning rate decay, and stability mechanisms for adaptive transfer learning.
- ▸ Experimental results demonstrate significant improvements in BLEU-4 scores on three datasets.
- ▸ HATL addresses challenges in SLMT, including scarce datasets, limited signer diversity, and large domain gaps.
Merits
Strength
HATL's adaptive approach to transfer learning allows it to effectively address the challenges in SLMT, making it a promising solution for bridging communication between Deaf and hearing individuals.
Demerits
Limitation
The article primarily focuses on the technical aspects of HATL, with limited discussion on its practical implementation and potential societal impact.
Expert Commentary
The article's contribution to the field of SLMT is significant, as it addresses the critical challenges of scarce datasets, limited signer diversity, and large domain gaps. However, further research is needed to explore the practical implementation and societal impact of HATL. Additionally, the article's focus on the technical aspects of HATL may limit its appeal to readers outside of the machine learning community. Nevertheless, the proposed framework has the potential to make a tangible difference in the lives of Deaf and hard-of-hearing individuals, and its implications for policy and law should not be underestimated.
Recommendations
- ✓ Future research should focus on exploring the practical implementation and societal impact of HATL, including its potential applications in real-world settings.
- ✓ The development of HATL should be accompanied by a critical examination of its ethical and policy implications, particularly in the areas of accessibility and disability rights.
Sources
Original: arXiv - cs.AI
