arXiv — NLP / Computation & Language · · 3 min read

ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation

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Computer Science > Computation and Language

arXiv:2605.15794 (cs)
[Submitted on 15 May 2026]

Title:ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation

View a PDF of the paper titled ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation, by Micha{\l} Ciesi\'o{\l}ka and 3 other authors
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Abstract:We present ForMaT (Format-Preserving Multilingual Translation), a parallel corpus of 3,956 PDFs across 15 language pairs that preserves original layout metadata proposed for multimodal machine translation. To ensure structural diversity in the dataset, we employ K-Medoids sampling over 45 geometric features, capturing complex elements like nested tables and formulas to focus only on visually diverse PDF documents. Our evaluation reveals that current MT systems struggle with spatial grounding and geometric synchronization, often losing the link between text and its visual context. ForMaT provides a benchmark for developing layout-aware translation models that integrate visual and textual context for high-fidelity document reconstruction.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2605.15794 [cs.CL]
  (or arXiv:2605.15794v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2605.15794
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Kamil Guttmann [view email]
[v1] Fri, 15 May 2026 09:50:37 UTC (2,966 KB)
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