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

📰 ArXiv cs.AI

HATL is a hierarchical adaptive-transfer learning framework for sign language machine translation that addresses scarce datasets and limited signer diversity

advanced Published 23 Mar 2026

Action Steps

Develop a hierarchical framework to adaptively transfer knowledge from pre-trained models to sign language machine translation tasks
Utilize adaptive-transfer learning to preserve pre-trained representations and mitigate overfitting
Implement a mechanism to handle scarce datasets and limited signer diversity in sign language machine translation
Evaluate the performance of HATL on various sign language machine translation benchmarks

Who Needs to Know This

Machine learning researchers and engineers working on sign language machine translation can benefit from HATL to improve the accuracy and adaptability of their models, while software engineers and developers can utilize this framework to build more effective SLMT systems

Key Insight

💡 HATL addresses the challenges of scarce datasets and limited signer diversity in sign language machine translation by using a hierarchical adaptive-transfer learning approach