ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation
Michał Ciesiółka, Dawid Wiśniewski, Adrian Charkiewicz, Kamil Guttmann
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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.- Anthology ID:
- 2026.eamt-1.12
- Volume:
- Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
- Month:
- June
- Year:
- 2026
- Address:
- Tilburg, The Netherlands
- Editors:
- Dimitar Shterionov, Eva Vanmassenhove, Mirella De Sisto, Fred Blain, Javad Pourmostafa Roshan Sharami, Lisa Lepp, Chiara Manna, Argentina Anna Rescigno, Alina Karakanta, Ayla Rigouts Terryn, Manuel Lardelli, Natalia Resende, Elena Murgolo, Janiça Hackenbuchner, Anna Zaretskaya, Miquel Esplà-Gomis, Thierry Etchegoyhen, Dagmar Gromann, Rachel Bawden, Barry Haddow, Sara Szoc, Mikel Forcada, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 143–157
- Language:
- URL:
- https://aclanthology.org/2026.eamt-1.12/
- DOI:
- Bibkey:
- Cite (ACL):
- Michał Ciesiółka, Dawid Wiśniewski, Adrian Charkiewicz, and Kamil Guttmann. 2026. ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 143–157, Tilburg, The Netherlands. European Association for Machine Translation.
- Cite (Informal):
- ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation (Ciesiółka et al., EAMT 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.eamt-1.12.pdf
Export citation
@inproceedings{ciesiolka-etal-2026-format,
title = "{F}or{M}a{T}: Dataset for Visually-Grounded Multilingual {PDF} Translation",
author = "Ciesi{\'o}{\l}ka, Micha{\l} and
Wi{\'s}niewski, Dawid and
Charkiewicz, Adrian and
Guttmann, Kamil",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.12/",
pages = "143--157",
ISBN = "9789403901411",
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."
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%0 Conference Proceedings %T ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation %A Ciesiółka, Michał %A Wiśniewski, Dawid %A Charkiewicz, Adrian %A Guttmann, Kamil %Y Shterionov, Dimitar %Y Vanmassenhove, Eva %Y De Sisto, Mirella %Y Blain, Fred %Y Pourmostafa Roshan Sharami, Javad %Y Lepp, Lisa %Y Manna, Chiara %Y Rescigno, Argentina Anna %Y Karakanta, Alina %Y Rigouts Terryn, Ayla %Y Lardelli, Manuel %Y Resende, Natalia %Y Murgolo, Elena %Y Hackenbuchner, Janiça %Y Zaretskaya, Anna %Y Esplà-Gomis, Miquel %Y Etchegoyhen, Thierry %Y Gromann, Dagmar %Y Bawden, Rachel %Y Haddow, Barry %Y Szoc, Sara %Y Forcada, Mikel %Y Moniz, Helena %S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1) %D 2026 %8 June %I European Association for Machine Translation %C Tilburg, The Netherlands %@ 9789403901411 %F ciesiolka-etal-2026-format %X 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. %U https://aclanthology.org/2026.eamt-1.12/ %P 143-157
Markdown (Informal)
[ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation](https://aclanthology.org/2026.eamt-1.12/) (Ciesiółka et al., EAMT 2026)
- ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation (Ciesiółka et al., EAMT 2026)
ACL
- Michał Ciesiółka, Dawid Wiśniewski, Adrian Charkiewicz, and Kamil Guttmann. 2026. ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 143–157, Tilburg, The Netherlands. European Association for Machine Translation.