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QT21: Quality Translation 21

Objective

A European Digital Single Market free of barriers, including language barriers, is a stated EU objective to be achieved by 2020. The findings of the META-NET Language White Papers show that currently only 3 of the EU-27 languages enjoy moderate to good support by our machine translation technologies, with either weak (at best fragmentary) or no support for the vast majority of the EU-27 languages. This lack is a key obstacle impeding the free flow of people, information and trade in the European Digital Single Market. Many of the languages not supported by our current technologies show common traits: they are morphologically complex, with free and diverse word order. Often there are not enough training resources and/or processing tools. Together this results in drastic drops in translation quality. The combined challenges of linguistic phenomena and resource scenarios have created a large and under-explored grey area in the language technology map of European languages. Combining support from key stakeholders, QT21 addresses this grey area developing (1) substantially improved statistical and machine-learning based translation models for challenging languages and resource scenarios, (2) improved evaluation and continuous learning from mistakes, guided by a systematic analysis of quality barriers, informed by human translators, (3) all with a strong focus on scalability, to ensure that learning and decoding with these models is efficient and that reliance on data (annotated or not) is minimised. To continuously measure progress, and to provide a platform for sharing and collaboration (QT21 internally and beyond), the project revolves around a series of Shared Tasks, for maximum impact co-organised with WMT. To support early technology transfer, QT21 proposes a Technology Bridge linking ICT-17(a) and (b) projects and opening up the possibility of showing technical feasibility of early research outputs in near to operational environments.
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Coordinator

DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH

Address

Trippstadter Strasse 122
67663 Kaiserslautern

Germany

Activity type

Research Organisations

EU Contribution

€ 932 625

Participants (13)

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RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN

Germany

EU Contribution

€ 314 679

UNIVERSITEIT VAN AMSTERDAM

Netherlands

EU Contribution

€ 274 089

DUBLIN CITY UNIVERSITY

Ireland

EU Contribution

€ 253 503

THE UNIVERSITY OF EDINBURGH

United Kingdom

EU Contribution

€ 358 215

KARLSRUHER INSTITUT FUER TECHNOLOGIE

Germany

EU Contribution

€ 359 061

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS

France

EU Contribution

€ 256 318

UNIVERZITA KARLOVA

Czechia

EU Contribution

€ 283 063

FONDAZIONE BRUNO KESSLER

Italy

EU Contribution

€ 322 500

THE UNIVERSITY OF SHEFFIELD

United Kingdom

EU Contribution

€ 290 625

TAUS BV

Netherlands

EU Contribution

€ 108 750

TEXT & FORM GMBH

Germany

EU Contribution

€ 107 500

TILDE SIA

Latvia

EU Contribution

€ 116 500

HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Hong Kong

Project information

Grant agreement ID: 645452

  • Start date

    1 February 2015

  • End date

    31 January 2018

Funded under:

H2020-EU.2.1.1.4.

  • Overall budget:

    € 3 997 428

  • EU contribution

    € 3 977 428

Coordinated by:

DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH

Germany