The document discusses machine translation (MT) applications in the public sector of the EU. It outlines various types of government-to-stakeholder communication that can benefit from MT, such as government-to-citizens, government-to-businesses, and government-to-governments. It also summarizes case studies on using MT to support multilingualism in the public sectors of Latvia and Lithuania. The document advocates for building an EU-wide public MT infrastructure to fulfill the promises of e-government and language diversity.

1. s
Andrejs Vasiļjevs
andrejs@tilde.com
MT Showcase | LocWorld2014 | Dublin | 04.06.2014
MT applications in
the EU public sector

2. • Language technology developer
• Localization service provider
• Leadership in smaller languages
• Offices in Riga (Latvia), Tallinn
(Estonia) and Vilnius (Lithuania)
• 130 employees
• Strong R&D team
• 9 PhDs and candidates
• Trusted partner of the EU for
significant research projects

3. G2C
Government to Citizens
G2C
Government to Businesses
G2E
Government to Employees
G2G
Government to Governments
C2G
Citizens to GovernmentseGovernment

4. • Better business
environment
• Customers on-line,
NOT in line
• Improving efficiency
• Increase participation
• Reach marginalized
groupsGoals of
e-Government

5. 80
European languages
24
official languages
1.1B EUR
EU spends per annum on
translation services
Multilingual
Europe

6. The five most
widely spoken
foreign
languages in
Europe
38%
12% 11%
7% 5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
English French German Spanish Russian
% of European Union population

7. Europeans
able to hold
a conversation
in additional
language
54%
25%
10%
0%
10%
20%
30%
40%
50%
60%
at least one at least two at least three
% of European Union population

8. 98%
Luxembourg
95%
Latvia
94%
Netherlands
93%
Malta
92%
Slovenia
Lithuania
91%
Sweden
EU countries
where people
can speak
at least
one language
in addition
to their mother
tongue

9. 65%
Hungary
62%
Italy
61%
United Kingdom
Portugal
60%
Ireland
EU countries
where majority of
people
can not
speak any
foreign language

10. What role does
translation play
in your everyday life?
is translation
important
?
43
16
30
% of EU population
important
very important
no role

11. ‘‘The language
of Europe is
translation’’
Umberto Eco

12. Preserving the European cultural
and linguistic diversity
Securing at affordable costs the
free flow of information and
thought across language
boundaries
Providing each language
community with the most
advanced technologies
… so that maintaining their mother
tongue does not turn into a
disadvantage
Credits: Hans Uszkoreit

13. EU MULTILINGUALITY
IN PRACTICE:
CASE STUDY
In October 2010, a Spanish lawyer turned to the Ombudsman,
complaining that many public consultations are only published
in English, for example, consultations concerning a new
partnership to help small and medium-sized enterprises and
concerning the freedom of movement of workers.

16. “The Commission should
ensure that all European
citizens are able to
understand its public
consultations,
which should [..]
be published in all the
official languages.
Its failure to do so is an
instance of
maladministration.”
4 October 2012
The European Ombudsman,
P. Nikiforos Diamandouros

17. [European] Commission [has] to ensure that every EU citizen's right to address the EU institutions
in any of the EU official languages is fully respected and implemented by ensuring that public
consultations are available in all EU official languages,[..] and that there is no language-based
discrimination [..]
European Parliament resolution 2012/2676(RSP)

18. Fulfill the vision of
e-Government
AND
the promise of
language diversity
The e-Government
Challenge

19. machine translation
machine translation

20. What MT
serves best
Short shelf life
Immediacy
Large volume
Multiple languages

21. Where it works
Embedded in web
pages
Multilingual online
services
Social media
Multi-lingual chat
Mobile devices

22. Customizable,
trainable
Domain specific
On-demand
In the cloud
Real –time
Security
PrivacySpecific
requirements

23. Case Study:
LATVIA
Case study: MT for eGov in Latvia

24. Population 2,1 M
1,6 million native Latvian
speakers
Large Russian speaking
population (36%)
Lack of parallel data
Complex language
structure
Highly inflected
Language
situation in
Latvia

25. • To provide e-services to
all the population /
linguistic groups
• To develop technologies
for supporting Latvian in
information society
• To facilitate access to the
information of European
Union institutions
• To integrate in the
infrastructure of EU
multilingual services
GOAL
MT @ eGov.LV

26. Online translation
service
Translation widget for
integration in eGov
service sites
Standardized API for
universal integratability
INTEGRATABILITY
MT @ eGov.LV

27. MT
@
eGov.LV

28. custom
machine
translation
as easy
and
affordable
as
a cup
of coffee

29. • upload your data
TMX, XLIFF, DOC, PPTX, XLSX,
PDF, XLZ, TXT
• combine it with the data on the
LetsMT public repository
• generate your custom MT
with a few mouse clicks
• run your MT system
on the LetsMT cloud
• use it in your CAT tool
with LetsMT plug-in
• integrate through LetsMT API
in online or desktop app

31. 2,4 billion
parallel sentences
5,8 billion
monolingual sentences
126
languages
548
MT systems
currently provided

32. Recently added:
EUBookshop data
3.6 billions
of words
from
135,000
documents
48
languages
>10 million
parallel sentences
for some language pairs

33. Data for SMT training

34. custom
terminology
incremental
data
custom
MT

35. Online Terminology Services
Translation
Training
SMT System
Training and
adaptation
Online Translation
Service
Input Text for
Translation
Parallel
corpus
Monolingual
corpus
Bilingual term
collections
Monolingual
Term
Extraction
Trained
SMT
Model
Bilingual
Term
Extraction
Translated
Text

36. Multiple translation
options
• copy texts into the
online translator
• upload entire files and
documents
• or translate in your
own work environment
(CAT tools)

38. .

40. Use case: MT to support
EU Presidency

41. Case Study:
LATVIA
Case study: MT as a Public Service in
Lithuania

42. 25.1%
28.5%
Czech Polish
32.9%
Latvian
Productivity evaluation
Productivity
evaluation

43. Estonian
Public
MT

45. Better than Google
Better than Google & Bing
37,38
44,15
28,8
38,42
24,22
37,97
35,04
42,92
18,59
32,56
21,45
35,3
26,95
37,32
16,86
33,09
17,42
30,14
16,7
26,11
0
5
10
15
20
25
30
35
40
45
50
English-Latvian Latvian-English English-Lithuanian Lithuanian-English English-Estonian Estonian-English
Tilde Google Microsoft Tartu Uni

46.  Deliver the strategic vision, functional, technical and
operational specifications of the infrastructure for
EU public service for automated translation and other
multilingual services and resources
 Design a sustainable governance model for
the multilingual infrastructure
 Foster multi-stakeholders alliances to ensure its
commitment and support for the deployment of the MLi
and its usage
Towards the EU
Public MT Infrastructure

47. Machine translation
bringing governments and citizens closer

48. tilde.com
andrejs@tilde.com
Author:-Copyright:Stocklib©RobertWilson