CzechDocs: A Multiway Parallel Dataset of Formatted Documents for Minority Languages in Czechia

20d ago · Global · primary source: export.arxiv.org

Researchers have released CzechDocs, a multiway parallel dataset of formatted documents aimed at improving machine translation for minority languages in Czechia, according to a paper submitted on 18 Jun 2026 [1]. The dataset covers Czech and minority languages used in Czechia, primarily Ukrainian and English, with smaller portions of Vietnamese, Russian and other languages [1]. It includes documents in HTML, DOCX, and PDF formats [1]. The project is designed to support the evaluation of machine translation systems that aim to preserve document formatting during translation [1]. A validation subset of the dataset and an evaluation toolkit have been publicly released for further research, while a held-out test split will be reserved for a future shared task focused on document-level translation with formatting preservation [2]. The paper was posted on arXiv, an open-access repository of electronic preprints that, as of November 2024, receives about 24,000 submissions per month [6]. arXiv hosts papers in fields including computer science, mathematics, and physics, and is not peer-reviewed [6]. The repository passed the two-million-article milestone by the end of 2021 [6]. The CzechDocs paper appears with a suite of experimental community tools under the arXivLabs framework, which allows collaborators to develop and share new features directly on the arXiv website [4]. arXivLabs was formalized in 2020 to enable collaborations that share arXiv’s values of openness, community, excellence, and user data privacy [4]. Third-party collaborators in the program have access only to minimal and anonymized user data, and any other use is strictly prohibited without written consent from arXiv [4]. Current arXivLabs projects include the Bibliographic Explorer, which displays citation networks for papers, and the CORE Recommender, which surfaces open-access papers from a global network of repositories [5]. The framework is currently on a hiatus for new proposals while the arXiv development team focuses on modernizing and moving its systems to the cloud [3].

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Background sources we checked (7)
  • arxiv.org ↗ We present CzechDocs, a multiway parallel dataset of formatted documents (HTML, DOCX, and PDF) covering Czech and minority languages used in Czechia-primarily Ukrainian and English, with smaller portions of Vietnamese, Russian and other languages. The dataset is designed to suppo…
  • info.arxiv.org ↗ arXiv Labs - arXiv info | arXiv e-print repository Skip to content # arXiv Labs Attention arXiv Users: arXiv Labs is pausing new proposals ## What are arXiv Labs? arXiv Labs are a way for the community to contribute new, useful features to arXiv. These integrations are avail…
  • blog.arxiv.org ↗ arXivLabs: a space for community innovation – arXiv blog arXiv has launched a new, formalized framework enabling innovative collaborations with individuals and organizations. “Members of our community want to contribute tools that enhance the arXiv experience, and we val…
  • info.arxiv.org ↗ arXivLabs: Showcase - arXiv info | arXiv e-print repository ... # arXivLabs: Showcase ... arXiv is surrounded by a community of researchers and developers working at the cutting edge of information science and technology. ... While the arXiv team is focused on our core mission—pr…
  • en.wikipedia.org ↗ arXiv (pronounced as "archive"—the X represents the Greek letter chi ⟨χ⟩) is an open-access repository of electronic preprints and postprints (known as e-prints) approved for posting after moderation, but not peer reviewed. It consists of scientific papers in the fields of mathem…
  • en.wikipedia.org ↗ 14 (fourteen) is the natural number following 13 and preceding 15.…
  • en.wikipedia.org ↗ A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.…

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