We introduce LuxMT, a machine translation system based on Gemma 3 27B and fine-tuned for translation from Luxembourgish (LB) into French (FR) and English (EN). To assess translation performance, we construct a novel benchmark covering LB-FR, LB-EN, and LB-FR using human-translated data from Luci, a tourist magazine about Luxembourg. Training data stems from LuxAlign, a parallel corpus of multilingual Luxembourgish news articles, and LB parliamentary transcripts augmented with Google Translate. We filter the data using LuxEmbedder, LB sentence embeddings, to remove low-equivalence segment-pairs. Overall, LuxMT's results suggest strong improvements over the Gemma 3 baseline, even for translating LB to German (DE), despite the training data not containing any DE. We also explore LuxEmbedder's potential to be used as a quality estimation metric and find strong correlations with other reference-based metrics. However, we call for further research to fully assess the metric's utility and advise using it with caution.

翻译：本文介绍LuxMT——一个基于Gemma 3 27B架构、专门针对卢森堡语(LB)到法语(FR)和英语(EN)翻译任务进行微调的机器翻译系统。为评估翻译性能，我们构建了一个涵盖LB-FR、LB-EN及LB-FR方向的新型评测基准，该基准采用卢森堡旅游杂志《Luci》的人工翻译数据。训练数据来源于多语言卢森堡语新闻平行语料库LuxAlign，以及通过谷歌翻译增强的卢森堡议会记录文本。我们使用卢森堡语句子嵌入模型LuxEmbedder对数据进行过滤，以去除等价性较低的句对。总体而言，LuxMT的实验结果表明，相较于Gemma 3基线模型，该系统在各项翻译任务中均取得显著提升——即使在训练数据未包含德语(DE)的情况下，其LB-DE翻译性能仍获得改善。我们还探索了将LuxEmbedder作为质量评估指标的潜力，发现其与基于参考译文的其他评估指标存在强相关性。然而，我们建议需开展进一步研究以全面评估该指标的实用性，并建议谨慎使用。