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arXiv · 2610.01082

Precision over Scale: A Polish-Silesian Benchmark and a Translation System Outperforming Open-Source and Commercial Models

Grzegorz Kulik·Mikołaj Pokrywka·Adam Jatowt·Wojciech Kusa
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Abstract

Dialectal machine translation remains challenging due to limited data and strong linguistic variation not captured by standard benchmarks, which often assume standardized and well-edited text. We study Polish-Silesian MT using neural and rule-based systems, evaluating on SiLTT - a new Pol-Szl testset, alongside established BOUQuET and FLORES benchmarks. Results show our rule-based system is consistently strongest on SiLTT and BOUQuET datasets and that TranslateGemma fine-tuned on a curated dataset improves over strong neural baselines but does not surpass the rule-based system in dialectal settings. We release SiLTT and our best neural model to support further research.

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Grzegorz Kulik, Mikołaj Pokrywka, Adam Jatowt, Wojciech Kusa. 2026-10-01. Precision over Scale: A Polish-Silesian Benchmark and a Translation System Outperforming Open-Source and Commercial Models. https://arxiv.org/abs/2610.01082

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