# Letz Translate: Low-Resource Machine Translation for Luxembourgish

arXiv: https://arxiv.org/abs/2303.01347 · HTML: https://mlsanity.com/paper/2303.01347/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 9 of 9)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Rapid Adaptation of Neural Machine Translation to New Languages](https://mlsanity.com/paper/1808.04189/) (2018)
- [Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data](https://mlsanity.com/paper/2105.15071/) (2021)
- [Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation](https://mlsanity.com/paper/1906.05683/) (2019)

## Built on by (most cited first, 5 of 5)

- [LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings](https://mlsanity.com/paper/2412.03331/) (2024)
- [Is Small Language Model the Silver Bullet to Low-Resource Languages Machine Translation?](https://mlsanity.com/paper/2503.24102/) (2025)
- [DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers](https://mlsanity.com/paper/2405.16482/) (2024)
- [Multi-Hypothesis Distillation of Multilingual Neural Translation Models for Low-Resource Languages](https://mlsanity.com/paper/2507.21568/) (2025)
- [LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish](https://mlsanity.com/paper/2606.31947/) (2026)

## Abstract (arXiv)

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.