Gradient-Informed Training for Low-Resource Multilingual Speech Translation

Ruiyan Sun; Satoshi Nakamura

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arXiv · 2603.25836

Gradient-Informed Training for Low-Resource Multilingual Speech Translation

Ruiyan Sun·Satoshi Nakamura

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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.

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Ruiyan Sun, Satoshi Nakamura. 2026-03-26. Gradient-Informed Training for Low-Resource Multilingual Speech Translation. https://arxiv.org/abs/2603.25836

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