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        <title>AI4Culture: Towards Multilingual Access for Cultural Heritage Data</title>
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            <title>Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)</title>
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    <abstract>The AI4Culture project (2023-2025), funded by the European Commission, and involving a 12-partner consortium led by the National Technical University of Athens, develops a platform serving as an online capacity building hub for AI technologies in the cultural heritage (CH) sector, enabling multilingual access to CH data. It offers access to AI-related resources, including openly labelled datasets for model training and testing, deployable and reusable tools, and capacity building materials. The tools are aimed at optical character recognition (OCR) for printed and handwritten documents, subtitle generation and validation, machine translation (MT), and metadata enrichment via image information extraction and semantic linking. The project also customises these tools to enhance interface and component usability. We illustrate this with technology that corrects OCR output using language models and adapts it for MT.</abstract>
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%0 Conference Proceedings
%T AI4Culture: Towards Multilingual Access for Cultural Heritage Data
%A Vanallemeersch, Tom
%A Szoc, Sara
%A Meeus, Laurens
%Y Scarton, Carolina
%Y Prescott, Charlotte
%Y Bayliss, Chris
%Y Oakley, Chris
%Y Wright, Joanna
%Y Wrigley, Stuart
%Y Song, Xingyi
%Y Gow-Smith, Edward
%Y Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F vanallemeersch-etal-2024-ai4culture
%X The AI4Culture project (2023-2025), funded by the European Commission, and involving a 12-partner consortium led by the National Technical University of Athens, develops a platform serving as an online capacity building hub for AI technologies in the cultural heritage (CH) sector, enabling multilingual access to CH data. It offers access to AI-related resources, including openly labelled datasets for model training and testing, deployable and reusable tools, and capacity building materials. The tools are aimed at optical character recognition (OCR) for printed and handwritten documents, subtitle generation and validation, machine translation (MT), and metadata enrichment via image information extraction and semantic linking. The project also customises these tools to enhance interface and component usability. We illustrate this with technology that corrects OCR output using language models and adapts it for MT.
%U https://aclanthology.org/2024.eamt-2.30
%P 59-60

Markdown (Informal)

[AI4Culture: Towards Multilingual Access for Cultural Heritage Data](https://aclanthology.org/2024.eamt-2.30) (Vanallemeersch et al., EAMT 2024)

• AI4Culture: Towards Multilingual Access for Cultural Heritage Data (Vanallemeersch et al., EAMT 2024)

ACL

• Tom Vanallemeersch, Sara Szoc, and Laurens Meeus. 2024. AI4Culture: Towards Multilingual Access for Cultural Heritage Data. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2), pages 59–60, Sheffield, UK. European Association for Machine Translation (EAMT).