Automated Gradient-Driven Parameter Sharing for Low-Resource Multilingual Speech-to-Text Translation

Excerpt

arXiv:2603.25836v2 Announce Type: replace Abstract: In low-resource multilingual speech-to-text translation, uniform architectural sharing across languages frequently introduces representation conflicts that impede convergence. This work proposes a principled methodology to automatically determine layer-specific sharing patterns by mining training gradient information. Our approach employs three distinct analysis strategies: distance-based language clustering, self/cross-task divergence metrics