Automated Gradient-Driven Parameter Sharing for Low-Resource Multilingual Speech-to-Text Translation
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 for capacity allocation, and joint factorization coupled with canonical correlation analysis for subspace alignment. Extensive evaluation across four language pairs (using the SeamlessM4T-Medium architecture) demonstrates persistent improvements in ...
| Authors | Ruiyan Sun, Satoshi Nakamura |
|---|---|
| Primary category | cs.CL |
| Published | 26 March 2026 |
| Last updated at source | 17 September 2026 |
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