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Data4ML

Financiado

A prototype system for obtaining and managing training data for multilingual lea...

It is difficult to build high quality machine translation systems for less-resourced languages, such as the minority languages of Europe. State-of-the-art machine translation is trained on large parallel corpora, texts and their t... ver más

31/03/2025

LMU MUENCHEN

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-03-30

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2022-POC2: ERC PROOF OF CONCEPT GRANTS2

I+D

Cerrada hace 2 años

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2 Participantes

LMU MUENCHEN

150.00K€ | Lider

TUM

150.00K€ | Participante

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 24 meses Fecha Inicio: 2023-03-30
Fecha Fin: 2025-03-31

Líder del proyecto

LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN

TRL 4-5

Presupuesto del proyecto 150K€

Descripción del proyecto It is difficult to build high quality machine translation systems for less-resourced languages, such as the minority languages of Europe. State-of-the-art machine translation is trained on large parallel corpora, texts and their translations. But such corpora are not available for less-resourced languages. We will provide a system for the rapid and inexpensive creation of new parallel corpora. Our PoC project will both produce an open-source prototype utilizing findings from the PI's ERC StG, and determine IPR and future funding. The key innovation of the prototype will be that it can be used by the less-resourced language community themselves. Current systems require extensive background in natural language processing. Allowing the community to create and curate parallel data has clear social benefits. The creation of high quality machine translation systems for less-resourced languages will allow for more content creation in these languages, playing a strong role in the preservation of these languages. Curated parallel data will also be useful in activities such as education and cultural heritage research. Government funding is available for digital language preservation for many of the 7000 languages spoken on Earth. Companies with online translation systems such as Google and DeepL/Linguee are not addressing this market, as the ROI is too low. It makes more sense to empower local communities to create such parallel data. We will carefully evaluate our prototype to ensure that it meets their needs. Along with the creation of the prototype, we will determine how best to structure the IPR to support future development. Consulting, which we have already carried out for the Sorbian community, and a certification scheme for users of our system are two possibilities we will consider, along with commercial machine translation and multilingual classification problems such as hate speech detection.

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