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

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 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 translation quality metrics.

Computation and Language (cs.CL)

arXiv:2603.25836 [cs.CL]

(or arXiv:2603.25836v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.25836 Focus to learn more arXiv-issued DOI via DataCite

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