mlsanityData to 14 Oct 2026

The Mediomatix Corpus: Parallel Data for Romansh Language Varieties via Comparable Schoolbooks

arXiv 2508.16371 ↗First posted 22 Aug 2025cs.CLcited by 3

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Abstract (arXiv)

The five idioms (i.e., varieties) of the Romansh language are largely standardized and are taught in the schools of the respective communities in Switzerland. In this paper, we present the first parallel corpus of Romansh idioms. The corpus is based on 291 schoolbook volumes, which are comparable in content for the five idioms. We use automatic alignment methods to extract 207k multi-parallel segments from the books, with more than 2M tokens in total. A small-scale human evaluation confirms that the segments are highly parallel, making the dataset suitable for NLP applications such as machine translation between Romansh idioms. We release the parallel and unaligned versions of the dataset under a CC-BY-NC-SA license and demonstrate its utility for machine translation by training and evaluating an LLM and a supervised multilingual MT model on the dataset.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 22 Aug 2025.

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11 of 30 references.

References · 11

  1. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  2. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,261
  3. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 560
  4. 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 243
  5. 2019WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipediacited by 223
  6. 2019Facebook FAIR's WMT19 News Translation Task Submissioncited by 179
  7. 2019Domain Robustness in Neural Machine Translationcited by 65
  8. 2019The Effect of Translationese in Machine Translation Test Setscited by 49
  9. 2023SwissBERT: The Multilingual Language Model for Switzerlandcited by 9
  10. 2023Does mBERT understand Romansh? Evaluating word embeddings using word alignmentcited by 3
  11. 2024Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documentscited by 2

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3 papers.