  Natural language processing of Low-Resource Languages (LRL) is often
challenged by the lack of data. Therefore, achieving accurate machine
translation (MT) in a low-resource environment is a real problem that requires
practical solutions. Research in multilingual models have shown that some LRLs
can be handled with such models. However, their large size and computational
needs make their use in constrained environments (e.g., mobile/IoT devices or
limited/old servers) impractical. In this paper, we address this problem by
leveraging the power of large multilingual MT models using knowledge
distillation. Knowledge distillation can transfer knowledge from a large and
complex teacher model to a simpler and smaller student model without losing
much in performance. We also make use of high-resource languages that are
related or share the same linguistic root as the target LRL. For our
evaluation, we consider Luxembourgish as the LRL that shares some roots and
properties with German. We build multiple resource-efficient models based on
German, knowledge distillation from the multilingual No Language Left Behind
(NLLB) model, and pseudo-translation. We find that our efficient models are
more than 30\% faster and perform only 4\% lower compared to the large
state-of-the-art NLLB model.

Computation and Language;Software Engineering