Opendata, web and dolomites

GoURMET SIGNED

Global Under-Resourced MEedia Translation

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "GoURMET" data sheet

The following table provides information about the project.

Coordinator
THE UNIVERSITY OF EDINBURGH 

Organization address
address: OLD COLLEGE, SOUTH BRIDGE
city: EDINBURGH
postcode: EH8 9YL
website: www.ed.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 2˙906˙098 €
 EC max contribution 2˙906˙098 € (100%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2018-2
 Funding Scheme RIA
 Starting year 2019
 Duration (year-month-day) from 2019-01-01   to  2021-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE UNIVERSITY OF EDINBURGH UK (EDINBURGH) coordinator 948˙047.00
2    BRITISH BROADCASTING CORPORATION UK (LONDON) participant 549˙462.00
3    DEUTSCHE WELLE DE (BONN) participant 532˙500.00
4    UNIVERSITEIT VAN AMSTERDAM NL (AMSTERDAM) participant 490˙567.00
5    UNIVERSIDAD DE ALICANTE ES (ALICANTE) participant 385˙521.00

Map

 Project objective

Machine translation (MT) is an increasingly important technology for supporting communication in a globalised world. MT technology has gradually increased over the last ten years, but recent advances in neural machine translation (NMT), have resulted in significant interest in industry and have lead to very rapid adoption of the new paradigm (eg. Google, Facebook, UN, World International Patent Office). Although these models have shown significant advances in state-of-the-art performance they are data intensive and require parallel corpora of many millions of human translated sentences for training. Neural Machine translation is currently not able to deliver usable translations for the vast majority of language pairs in the world. This is especially problematic for our user partners, the BBC and DW who need access to fast and accurate translation for languages with very few resources.

The aim of GoURMET is to significantly improve the robustness and applicability of neural machine translation for low-resource language pairs and domains.

GoURMET has five objectives: - Development of a high-quality machine translation for under-resourced language pairs and domains; - Adaptable to new and emerging languages and domains; - Development of tools for analysts and journalists; - Sustainable, maintainable platform and services; - Dissemination and communication of project results to stakeholders and user group.

The project will focus on two use cases: - Global content creation - managing content creation in several languages efficiently by providing machine translations for correction by humans; - Media monitoring for low resource language pairs - tools to address the challenge of international news monitoring problem.

The outputs of the project will be field-tested at partners BBC and DW, and the platform will be further validated through innovation intensives such as the BBC NewsHack.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "GOURMET" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "GOURMET" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.2.1.1.)

EuConNeCts4 (2019)

European Conferences on Networks and Communications (EuCNC)

Read More  

5G-DIVE (2019)

5G-DIVE: eDge Intelligence for Vertical Experimentation

Read More  

ADEPTNESS (2020)

Design-Operation Continuum Methods for Testing and Deployment under Unforeseen Conditions for Cyber-Physical Systems of Systems

Read More