Efficient Continual Learning in Neural Machine Translation: A Low-Rank Adaptation Approach

• URL: http://arxiv.org/abs/2512.09910v1
• Date: Wed, 10 Dec 2025 18:37:57 GMT
• Title: Efficient Continual Learning in Neural Machine Translation: A Low-Rank Adaptation Approach
• Authors: Salvador Carrión, Francisco Casacuberta,
• Abstract summary: Continual learning in Neural Machine Translation (NMT) faces the dual challenges of catastrophic forgetting and the high computational cost of retraining.<n>This study establishes Low-Rank Adaptation (LoRA) as a parameter-efficient framework to address these challenges.
• Score: 0.4870012761464388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual learning in Neural Machine Translation (NMT) faces the dual challenges of catastrophic forgetting and the high computational cost of retraining. This study establishes Low-Rank Adaptation (LoRA) as a parameter-efficient framework to address these challenges in dedicated NMT architectures. We first demonstrate that LoRA-based fine-tuning adapts NMT models to new languages and domains with performance on par with full-parameter techniques, while utilizing only a fraction of the parameter space. Second, we propose an interactive adaptation method using a calibrated linear combination of LoRA modules. This approach functions as a gate-free mixture of experts, enabling real-time, user-controllable adjustments to domain and style without retraining. Finally, to mitigate catastrophic forgetting, we introduce a novel gradient-based regularization strategy specifically designed for low-rank decomposition matrices. Unlike methods that regularize the full parameter set, our approach weights the penalty on the low-rank updates using historical gradient information. Experimental results indicate that this strategy efficiently preserves prior domain knowledge while facilitating the acquisition of new tasks, offering a scalable paradigm for interactive and continual NMT.

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