Description du projet
Un prototype pour aider les communautés linguistiques disposant de moins de ressources
La génération et la gestion de données parallèles jouent un rôle dans la création de systèmes de traduction automatique de haute qualité, en particulier pour les langues disposant de moins de ressources. Elles permettent de soutenir la création de contenu et de préserver ces langues. Les entreprises s’abstiennent toutefois d’investir sur ce marché en raison du faible retour sur investissement. Le projet Data4ML, financé par le CER, développera une méthode rentable pour générer de nouveaux corpus parallèles. Il impliquera la création d’un prototype libre qui intégrera les connaissances acquises dans le cadre du projet de subvention de démarrage du CER du chercheur, et il répondra aux préoccupations en matière de droits de propriété intellectuelle et garantira un financement futur. Ce prototype pourrait autonomiser les communautés linguistiques disposant de ressources limitées. Le projet se penche également sur la traduction automatique commerciale et aborde les questions de classification multilingue, y compris l’identification des discours haineux.
Objectif
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.
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Appel à propositions
(s’ouvre dans une nouvelle fenêtre) ERC-2022-POC2
Voir d’autres projets de cet appelRégime de financement
HORIZON-ERC-POC - HORIZON ERC Proof of Concept GrantsInstitution d’accueil
80333 Muenchen
Allemagne