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TraMOOC

Translation for Massive Open Online Courses

Total Cost €

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EC-Contrib. €

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Partnership

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 TraMOOC project word cloud

Explore the words cloud of the TraMOOC project. It provides you a very rough idea of what is the project "TraMOOC" about.

nature    parallel    output    eleven    ing    english    constitutes    machine    schema    employed    peoples    tested    hard    mining    vector    translation    reaching    iversity    bric    efficient    assignments    lecture    time    ru    setup    citizens    syntax    sentiment    pt    size    diversity    language    de    constitute    videolectures    posts    mooc    digital    adding    bootstrapping    data    pl    barrier    zh    reveal    impediment    types    platform    bg    moocs    quality    net    translate    massive    cs    tramooc    du    blog    languages    quantitative    refine    implicit    experts    showcased    complying    combined    library    presentations    models    accurate    phrase    automatic    domain    educating    detection    performed    supporting    source    innovative    human    weak    separate    regarding    retrain    tackling    courses    methodology    feedback    crowdsourcing    mt    cr    infrastructure    subtitles    tests    modal    opinion    qualitative    translated    genre    texts    strict    video    el    lists    extensive    evaluation    entity    online    statistical    independent   

Project "TraMOOC" data sheet

The following table provides information about the project.

Coordinator
HUMBOLDT-UNIVERSITAET ZU BERLIN 

Organization address
address: UNTER DEN LINDEN 6
city: BERLIN
postcode: 10117
website: www.hu-berlin.de

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 Germany [DE]
 Project website http://tramooc.eu/
 Total cost 3˙223˙835 €
 EC max contribution 3˙081˙147 € (96%)
 Programme 1. H2020-EU.2.1.1.4. (Content technologies and information management: ICT for digital content, cultural and creative industries)
 Code Call H2020-ICT-2014-1
 Funding Scheme IA
 Starting year 2015
 Duration (year-month-day) from 2015-02-01   to  2018-01-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    HUMBOLDT-UNIVERSITAET ZU BERLIN DE (BERLIN) coordinator 690˙565.00
2    THE UNIVERSITY OF EDINBURGH UK (EDINBURGH) participant 463˙310.00
3    DUBLIN CITY UNIVERSITY IE (DUBLIN) participant 434˙815.00
4    IONIAN UNIVERSITY EL (CORFU) participant 418˙190.00
5    STICHTING KATHOLIEKE UNIVERSITEIT NL (NIJMEGEN) participant 319˙550.00
6    KNOWLEDGE 4 ALL FOUNDATION LBG UK (REDHILL) participant 277˙250.00
7    DELUXE MEDIA EUROPE LTD UK (LONDON) participant 234˙937.00
8    STICHTING KATHOLIEKE UNIVERSITEIT BRABANT NL (TILBURG) participant 144˙530.00
9    IVERSITY GMBH DE (Bernau bei Berlin) participant 56˙000.00
10    EASN Technology Innovation Services BVBA BE (BUDINGEN) participant 42˙000.00

Map

 Project objective

Massive Open Online Courses have been growing rapidly in size and impact. Yet the language barrier constitutes a major growth impediment in reaching out to all peoples and educating all citizens. TraMOOC aims at tackling this impediment by developing high-quality translation of all types of text genre included in MOOCs (e.g. assignments, tests, presentations, lecture subtitles, blog text) from English into eleven European and BRIC languages (DE, IT, PT, EL, DU, CS, BG, CR, PL, RU, ZH) that constitute strong use cases, are hard to translate into and have weak MT support, thus complying with the call objectives. Phrase-based and syntax-based statistical machine translation models will be developed for addressing language diversity and supporting the language-independent nature of the methodology. For a high quality, automatic translation approach and for adding value to existing infrastructure, extensive advanced bootstrapping of new resources will be performed. An innovative multi-modal automatic and human evaluation schema will further ensure translation quality. For human evaluation, an innovative, strict-access control, time- and cost-efficient crowdsourcing setup will be used. Translation experts, domain experts and end users will also be involved. Separate task mining applications will be employed for implicit translation evaluation: (i) topic detection will be applied to source and translated texts and the resulting entity lists will be compared, leading to further qualitative and quantitative translation evaluation results; (ii) sentiment analysis performed on MOOC users’ blog posts will reveal end user opinion/evaluation regarding translation quality. Results will be combined into a feedback vector and used to refine parallel data and retrain translation models towards a more accurate second-phase translation output. The project results will be showcased and tested on the Iversity MOOC platform and on the VideoLectures.NET digital video lecture library.

 Deliverables

List of deliverables.
Translation prototype version 2 Demonstrators, pilots, prototypes 2019-05-31 12:41:52
Translation prototype version 1 Demonstrators, pilots, prototypes 2019-05-31 12:41:41
Data management plan Open Research Data Pilot 2019-05-31 12:42:26
Final field testing report Documents, reports 2019-05-31 12:42:17
Report on the human and automatic first stage translation evaluation Documents, reports 2019-05-31 12:41:54
System prototype version 3 Demonstrators, pilots, prototypes 2019-05-31 12:42:15
Quality and risk management plan Documents, reports 2019-05-31 12:42:02
Report on the implicit translation evaluation Documents, reports 2019-05-31 12:42:05
Report on Crowdsourcing Activity 1 Documents, reports 2019-05-31 12:41:29
Dissemination and Exploitation plan of the project results Documents, reports 2019-05-31 12:42:26
System prototype version 2 Demonstrators, pilots, prototypes 2019-05-31 12:42:18
Scientific and commercial exploitation report Documents, reports 2019-05-31 12:42:27
Language resources and tools management plan Documents, reports 2019-05-31 12:42:10
Report on crowdsourcing activity 2 Documents, reports 2019-05-31 12:42:04
Report on crowdsourcing activity 3 Documents, reports 2019-05-31 12:42:03
Report on the human and automatic second stage translation evaluation Documents, reports 2019-05-31 12:41:58
Communication pack for the project legacy Documents, reports 2019-05-31 12:42:28
Initial field testing report Documents, reports 2019-05-31 12:42:09
Translation prototype version 3 Demonstrators, pilots, prototypes 2019-05-31 12:41:50
Crowdsourcing platform configuration Documents, reports 2019-05-31 12:42:30
Final Plan for the Dissemination and Exploitation of the project results Documents, reports 2019-05-31 12:42:30
Initial integration report Documents, reports 2019-05-31 12:42:07
System prototype version 1 Demonstrators, pilots, prototypes 2019-05-31 12:42:05
Initial system architecture Documents, reports 2019-05-31 12:42:30
Intermediate Dissemination and Exploitation plan of the project results Documents, reports 2019-05-31 12:42:27
Public website Demonstrators, pilots, prototypes 2019-05-31 12:42:16
Report on machine translation 3 Documents, reports 2019-05-31 12:41:54
Report on machine translation 1 Documents, reports 2019-05-31 12:41:28
Report on machine translation 2 Documents, reports 2019-05-31 12:41:54
Initial communication pack Documents, reports 2019-05-31 12:42:21
Final system architecture Documents, reports 2019-05-31 12:42:27
Impact and continuation plans for TraMOOC services and software Documents, reports 2019-05-31 12:42:14
Report on available MT structure Documents, reports 2019-05-30 16:59:28
Report on the infrastructure Documents, reports 2019-05-30 16:59:32

Take a look to the deliverables list in detail:  detailed list of TraMOOC deliverables.

 Publications

year authors and title journal last update
List of publications.
2017 Maja Popovic (UBER)
Comparing Language Related Issues for NMT and PBMT between German and English
published pages: 209-220, ISSN: 0032-6585, DOI:
Proceedings of the 20th Annual Conference of the European Association for Machine Translation (EAMT2017) (The Prague Bulletin of Mathematical Linguistics 108, June 2017) May 2017 2019-05-30
2015 Valia Kordoni (UBER)
TraMOOC Project overview
published pages: 217, ISSN: , DOI:
Proceedings of the 18th Annual Conference of the European Association for Machine Translation (EAMT2015) May 2015 2019-05-30
2017 Sheila Castilho, Joss Moorkens, Federico Gaspari, Iacer Calixto, John Tinsley, Andy Way
Is Neural Machine Translation the New State of the Art?
published pages: 109-120, ISSN: 1804-0462, DOI: 10.1515/pralin-2017-0013
The Prague Bulletin of Mathematical Linguistics 108/1 2019-05-30
2017 Vilelmini Sosoni (IURC)
Casting some Light on Experts’ Experience with Translation Crowdsourcing
published pages: 362 - 384, ISSN: 1740-357X, DOI:
Journal of Specialised Translation, Issue 28 July 2017 2019-05-30
2015 Alexandra Birch (UEDIN)
Mixed-Domain vs. Multi-Domain Statistical Machine Translation
published pages: , ISSN: , DOI:
Proceedings of Machine Translation Summit (MT Summit) October 2015 2019-05-30
2015 R. Sennrich (UEDIN), Barry Haddow (UEDIN)
A Joint Dependency Model of Morphological and Syntactic Structure for Statistical Machine Translation
published pages: , ISSN: , DOI:
Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2015) September 2015 2019-05-30
2016 Maja Popovic (UBER), Mihael Arčan (UBER), Arle Lommel (UBER)
Potential and Limits of Using Post-edits as Reference Translations for MT Evaluation
published pages: 218-229, ISSN: 2255-8950, DOI:
Baltic Journal of Modern Computing (Proceedings of the 19th Annual Conference of the European Association of Machine Translation) Vol. 4 2019-05-30

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