mlsanityData to 7 Oct 2026

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

arXiv 2610.01082 ↗First posted 1 Oct 2026cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 48 references that are in the map. First posted on arXiv 1 Oct 2026.

Builds on

16 of 48 references.

References · 16

  1. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  2. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
  3. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  4. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  5. 2020COMET: A Neural Framework for MT Evaluationcited by 1,024
  6. 2020Beyond English-Centric Multilingual Machine Translationcited by 559
  7. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
  8. 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
  9. 2023MADLAD-400: A Multilingual And Document-Level Large Audited Datasetcited by 180
  10. 2019CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEBcited by 177
  11. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 108
  12. 2021Survey of Low-Resource Machine Translationcited by 92
4 more
  1. 2022Democratizing Neural Machine Translation with OPUS-MTcited by 57
  2. 2025BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translationcited by 21
  3. 2019Adapting Multilingual Neural Machine Translation to Unseen Languagescited by 12
  4. 2025Bielik 11B v3: Multilingual Large Language Model for European Languagescited by 6

Built on by

Not cited yet.