Georg Rehm. AI for Translation Technologies and Multilingual Europe. DG TRAD Conference - Translation Services in the Digital World: A Sneak Peek into the (near) Future. Luxembourg. October 16/17, 2017.

1. Georg Rehm
georg.rehm@dfki.de
DFKI GmbH, Language Technology Lab – Berlin, Germany
META-NET, General Secretary
AI for Translation Technologies
and Multilingual Europe

2. Outline
• Artificial Intelligence
• Technology Support for Multilingual Europe
• European MT Research – Results from QT21
• Connecting Europe Facility – Automated Translation
• Towards the Human Language Project
• Conclusions
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3. Artificial Intelligence
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Data Intelligence
Current breakthroughs based on Machine Learning (Deep Learning)
Also still in use: symbolic, rule-based methods and systems
Artificial Intelligence
• Huge data sets + powerful algorithms + extremely fast hardware
• Self-driving cars, robots, image recognition, machine translation
• Enormous potential for disruptions in all sectors and areas

9. Technology Support for
Multilingual Europe
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10. • Multilingualism is at the very heart of the European idea
• 24 EU languages – all languages have the same status
• Dozens of regional and minority languages as well as
languages of immigrants and trade partners
• Economic challenges:
– If the DSM is not multilingual, there will be 20+ isolated markets
– Language barriers are market barriers
• Social and public challenges:
– Empower all citizens to use their mother tongues
– Enable cross-border, cross-lingual, cross-cultural communication
– Provide multilingual digital public services
– Restore trust in media (fake news debate, filter bubble issue etc.)

11. q
60 research centres in 34 countries (founded in 2010)
Chair of Executive Board: Jan Hajic (CUNI)
Dep.: J. van Genabith (DFKI), A. Vasiljevs (Tilde)
General Secretary: Georg Rehm (DFKI)
q
Multilingual Europe
Technology Alliance.
826 members in
67 countries
(published in 2013) (31 volumes; published in 2012)
T4ME (META-NET) CESAR METANET4UMETA-NORDMultilingual Europe Technology AllianceNET

12. q Basque
q Bulgarian*
q Catalan
q Croatian*
q Czech*
q Danish*
q Dutch*
q English*
q Estonian*
q Finnish*
q French*
q Galician
q German*
q Greek*
q Hungarian*
q Icelandic
q Irish*
q Italian*
q Latvian*
q Lithuanian*
q Maltese*
q Norwegian
q Polish*
q Portuguese*
q Romanian*
q Serbian
q Slovak*
q Slovene*
q Spanish*
q Swedish*
q Welsh
* Official EU languagehttp://www.meta-net.eu/whitepapers

13. MT
English
good
French, Spanish
moderate fragmentary
Catalan, Dutch, German,
Hungarian, Italian, Polish,
Romanian
weak or no support through LT
Basque, Bulgarian, Croatian,
Czech, Danish, Estonian, Finnish,
Galician, Greek, Icelandic, Irish,
Latvian, Lithuanian, Maltese,
Norwegian, Portuguese, Serbian,
Slovak, Slovene, Swedish, Welsh
excellent
Czech, Dutch,
Finnish, French,
German, Italian,
Portuguese,
Spanish
moderate fragmentary
Basque, Bulgarian, Catalan,
Danish, Estonian, Galician,
Greek, Hungarian, Irish,
Norwegian, Polish, Serbian,
Slovak, Slovene, Swedish
weak or no support through LT
Croatian, Icelandic, Latvian,
Lithuanian, Maltese, Romanian,
Welsh
excellent
English
good
Speech
English
good
Dutch, French,
German, Italian,
Spanish
moderate fragmentary
Basque, Bulgarian, Catalan,
Czech, Danish, Finnish,
Galician, Greek, Hungarian,
Norwegian, Polish,
Portuguese, Romanian,
Slovak, Slovene, Swedish
weak or no support through LT
Croatian, Estonian, Icelandic, Irish,
Latvian, Lithuanian, Maltese,
Serbian, Welsh
excellent
English
good
Czech, Dutch,
French, German,
Hungarian, Italian,
Polish, Spanish,
Swedish
moderate fragmentary
Basque, Bulgarian, Catalan,
Croatian, Danish, Estonian,
Finnish, Galician, Greek,
Norwegian, Portuguese,
Romanian, Serbian, Slovak,
Slovene
Icelandic, Irish, Latvian,
Lithuanian, Maltese, Welsh
weak or no support through LTexcellent
ResourcesTextAnalytics

14. Fragmentary
Weak/none
Moderate
Good
Excellent
Welsh
Maltese
Lithuanian
Latvian
Icelandic
Irish
Croatian
Serbian
Estonian
Slovene
Slovak
Romanian
Norwegian
Greek
Galician
Danish
Bulgarian
Basque
Swedish
Portuguese
Finnish
Catalan
Polish
Hungarian
Czech
Italian
German
Dutch
Spanish
French
English
Levelofsupport
Languages with names in red
have little or no MT support
Source: META-NET White Paper Series: Europe's Languages in the Digital Age. Springer, Heidelberg,
New York, Dordrecht, London, September 2012. Georg Rehm and Hans Uszkoreit (series editors)
Important: even current state of the art
technologies are far from being perfect!
Important: 20+ European languages are
severely under-supported and face the
danger of digital extinction.

15. Excellent
Good
Moderate
Fragmentary
Weak/no
support
LanguageTechnologySupport
MillionsofNativeSpeakers(Worldwide)
Yiddish
Welsh
VlaxRomani
Turkish
Scots
Romany
Occitan
Maltese
Macedonian
Luxembourgish
Lithuanian
Limburgish
Latvian
Icelandic
Friulian
Frisian
Breton
Bosnian
Asturian
Albanian
Irish
Croatian
Serbian
Hebrew
Estonian
Slovene
Slovak
Romanian
Norwegian
Greek
Galician
Danish
Bulgarian
Basque
Swedish
Portuguese
Finnish
Catalan
Polish
Hungarian
Czech
Italian
German
Dutch
Spanish
French
English
0
50
100
150
200
250
300
350
400
Source: Georg Rehm, Hans Uszkoreit, Ido Dagan, Vartkes Goetcherian, Mehmet Ugur Dogan, Coskun Mermer, Tamás Váradi, Sabine Kirchmeier-Andersen,
Gerhard Stickel, Meirion Prys Jones, Stefan Oeter, and Sigve Gramstad. An Update and Extension of the META-NET Study “Europe's Languages in the
Digital Age”. In Proceedings of the Workshop on Collaboration and Computing for Under-Resourced Languages in the Linked Open Data Era (CCURL 2014),
Reykjavik, Iceland, May 2014.

16. European Machine Translation
Research – Results from QT21
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17. Research & Innovation Action 2015-18
Coordinator:
Josef van Genabith (DFKI)
17

18. • Morphologically rich languages (De, Cz, Lv)
• Under-resourced languages (Lv, Ro)
• Quality Assessment: MQM – DQF
• Learning from human feedback (APE)
• Evaluation framework: WMT
– Event series to present and discuss results from MT evaluations
– Procedures: Automatic scoring (BLEU etc.) and human
judgements (large number of human annotators)
• Shared tasks (newspaper translation, quality estimation,
metrics and automatic post-editing)
QT21 is Improving Automatic Translation
18

19. Human Judgement Rankings
64 53 1 3First
66 65 3 6
First +
Second
2015 2016 2017
QT21 Best Online
19
WMT Newspaper Translation Task
• En ó Cz
• En ó De
• En ó Fr
• En ó Cz
• En ó De
• En ó Ro
• En ó Cz
• En ó De
• En ó Lv

20. 0
5
10
15
20
25
30
35
40
En -­> De De -­> En En -­> Cz Cz -­> En
QT21 improvement in the last 12 months vs. online systems
QT21-­WMT-­2016 Online WMT-­2017 QT21-­WMT-­2017
WMT 2016 System on WMT 2017 Data
20

21. • Data sets are the fuel for neural networks
• QT21’s neural technologies define the state of the art
• Ranked #1 in more than 80% of all tasks at WMT 2017
• Also predominantly ranked #1 at WMT 2016
• QT21 keeps commercial systems at a distance
• Huge improvements on morphologically rich languages
• MQM as a standard for quality evaluation
Selected Results
21

22. Connecting Europe Facility:
Automated Translation (CEF AT)
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23. Connecting Europe
23
• EU flagship goal: Establishing the Digital Single Market
• Overcoming existing barriers
– by creating an environment for digital services to flourish
– by providing cross-border infrastructures and services.
• Sectorial CEF Digital Service Infrastructures (DSIs)
This also includesODRBRIS eHealth EESSI
Citizens
need to
solve
disputes
online across
borders
Citizens and
business
partners need
legal certainty
when doing
business
cross-border
Citizens need
to have online
access to their
patient
summary when
abroad
Citizens need
to get to enjoy
their social
security
seamlessly
and online
when abroad
eProcurement
Open Data
e-Justice
Cyber Security
Safer Internet …

24. 24
• Technological CEF building blocks can be used by the
different DSIs (e.g., eInvoicing, eSignature etc.)
• Most important in this context: CEF eTranslation
– Why? To help European and national public administrations
exchange information across language barriers
– How? By providing MT capabilities that will enable digital
services (in particular all DSIs) to be multilingual.
• CEF eTranslation builds on MT@EC
• Guarantees confidentiality and security of translated data
• è ELRC contract
Connecting Europe
Coordinator:
Josef van Genabith (DFKI)

25. European Language
Resource Coordination
2525
• Language resourcesCollect
• Needs of public servicesIdentify
• With the public sector in the
identification of Language ResourcesEngage
• With any technical or legal issuesHelp
• Observatory for language resources
across EuropeAct

26. What has been achieved?
0
20
40
60
80
100
120
140
160
Bi-­/Multilingual Corpora Terminologies Monolingual Corpuora
LR contributions by type
Status: April 2017
• 225 language resources collected
• More than 2 billion words in all EU official
languages, Norwegian and Icelandic
• Over 450,000 terms
• More than 2 million translation units
• More than 91 resources to be used by you!

27. ELRC for you
27
• ELRC-SHARE Repository
– Access to, sharing and contribution of LRs
– Access to tools and services catalogue (forthcoming)
– http://www.lr-coordination.eu/resources
• ELRC Technical and Legal Helpdesk
– Support for potential data donors (phone, email)
– http://www.lr-coordination.eu/helpdesk
• ELRC On-site assistance
– http://www.lr-coordination.eu/services

28. Current Developments
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29. • Multilingual Europe: our languages enjoy equal status yet digital
extinction of the majority of EU languages is a very severe danger.
• Language Technology Research and Innovation in Europe:
World class research results (e.g., in QT21), strong SME base,
thousands of LSPs; fragmentation; need for coordination.
• Big need for high-quality Language Technologies: translation,
personal assistants, multilingual DSM etc. (example: CEF).
• AI: Important breakthroughs and massive investments in R&D and
applications (mostly in US, Asia) – huge opportunity for Europe!
• The European Language Challenge cannot be abandoned or
outsourced!
Ø Need for Language Technology made in Europe for Europe!
Current Developments
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30. Towards the
Human Language Project
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31. • STOA Workshop in European Parliament (January 2017):
“Language equality in the digital age – towards a Human Language Project”
• Human Language Project vision suggested in several presentations
• STOA Study, published in March 2017, does recommend setting up the HLP
Ø http://www.stoa.europarl.europa.eu/stoa/cms/home/workshops/language
STUDY
EPRS | European Parliamentary Research Service
Scientific Foresight Unit (STOA)
PE 581.621
Science and Technology Options Assessment

32. 32
• Goal: Deep Natural Language Understanding by 2030
• AI for Next Generation Language Technology
• Large-scale EU funding programme for basic and
applied research as well as innovation (10-15 years)
• New breakthroughs for research, industry and society
to foster a multitude of innovations.
Artificial Intelligence
including cognition, perception, vision,
cross-modal, cross-platform, cross-culture, IoT etc.
Machine Learning
Language TechnologyKnowledge Technology
EP DG Trad Conference (16/17 Oct. 2017) – AI for Translation Technologies
Human Language Project

33. • All official European and many additional languages
• Broad coverage, HQ, high precision – across modalities,
across platforms, across cultures
• Collaboration between EU, EC, EP, Member States,
research, industry, other stakeholders.
• Basic and applied research, innovation, commercialisation
• Policy change towards “LT-enabled multilingualism”
• HQMT – overcome quality (and language) barriers, written
and spoken, collaborate with human translators
• Resources and technologies for all European languages
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Human Language Project

34. http://www.cracker-project.eu • http://www.meta-net.eu
Version 1.0 of the SRIA
• Strategic Agenda: “Language Technologiesfor Multilingual
Europe – Towards a Human Language Project”
• Key recommendation: set up Human Language Project
• Also: establish Multilingual Digital Single Market
• Informed by “LT for Multilingual Europe” survey
• Takes into account: CEF AT, DSM, NGI
• To be presented at META-FORUM 2017 (13/14 Nov. 2017)
34

35. Summary & Conclusions
• AI is disrupting all industries – including translation.
• But: perfect machine translation is still far away.
• Not only are tools for gist translation getting better and
better, so are tools for human translators!
• Translators can expect to make use of a vastly improved
(adaptive) tool landscape in the next couple of years.
• We are collaborating with human translators better to
understand how translation processes work.
• The goal of the Human Language Project is to move
Europe into the pole position in this field.
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36. 13/14 November 2017
Brussels, Belgium – http://www.meta-forum.eu
Register now! Participation is free of charge.

37. Thank you!
Many thanks to Josef van Genabith, Christian Dugast,
Andrea Lösch (all DFKI) and to Maria Giagkou (ILSP).
Dr. Georg Rehm
DFKI Berlin
! georg.rehm@dfki.de
! http://de.linkedin.com/in/georgrehm
! https://www.slideshare.net/georgrehm
Human
Language
Project
Truly
Multilingual
Europe
European
Economy
(MDSM)
Attractive
jobs for
high
potentials
Education
and young
researchers
Massive
boost for
research
Foster
innovation
and new
companies
13/14 November 2017
Brussels, Belgium – http://www.meta-forum.eu
Register now! Participation is free of charge.