Language
Quality Game
Dynamics ERP
Translation
Solutions
Crowdsourcing Microsoft solution to validate Dynamics products’ user
interface terminology in context.
Provide access to existing Microsoft ERP linguistic assets through the Machine
Translator Hub (MTHUB) and a translation editor through the Multilingual
Application Toolkit (MAT)
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+40,000 forms
118,500 individual reviews 82% forms reviewed/76% OK
10 languages
Players: 89 from 33 Partners/83 MS ERP 1,640 bugs opened
All valid bugs (84%) fixed by RTM
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Player Site
https://lqg.partners.extranet.microsoft.com/v23/game/
Moderator Site
https://lqg.partners.extranet.microsoft.com/v23/moderator/
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Microsoft Translator Hub
Multilingual App Toolkit
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Microsoft Translator Hub for PLLP
CAT list
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Multilingual App Toolkit for PLLP
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Create MT hub project – partner specific instance
Train the MT Hub system with existing ERP/CRM
linguistic assets (TMs)

Machine translation
Project setup/Training
Microsoft
MBSi
MT Hub
Machine Translation


Reviewers

Localized Dynamics AX2012
Customers
Review and update translation
Translation
Partners
Translation Management:
 Recruit translators
 Select translation editor
(MAT for UI, other CAT tools, etc.)
 Source <-> XLIFF conversion (if MAT)
 Manage translation effort
Translator
Translation Process:
 Run machine translation
 Human review and post editing
of machine translated content
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www.microsoft.com/dynamics/axtechconference
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Microsoft Confidential
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An average of 4,000 forms
included per language 40,000 individual screen
captures
Participation by Microsoft
internal ERP Specialists
• 83 internal Players
• 72,362 individual reviews
screen (870 = average
screens review per player)
3,500
2,000
1,500
1,000
500
965
2,500
1,377
3,000
1,242
6 of the 10 languages
achieved 100% coverage
4,000
1,846
10 languages included German, French, Danish,
Russian, Chinese, Japanese,
Brazilian-Portuguese, Polish,
Czech, Lithuanian
4,500
2,279
82% of all screens across
the 10 languages were
reviewed by between 1-3
players
3,221
Participation from partners
• 89 Partner Players OnBoarded (33 companies
across 10 markets)
• 34 Partner Players
‘actively’ played
• 46,130 individual reviews
screen (1,350 average
screens review per player)
3,545
The Language Quality Game
was run during the month
of August and was ‘live’ for
a period of 3 weeks.
Coverage by Language
2,248
COVERAGE
4,133
PARTICIPATION
3,823
SCOPE
0
Reviewed - OK
Reviewed - Feedback Logged
Not Reviewed
Resolution of Translation Bugs
Duplicate, 215, 13%
Fixed,
1383,
84%
Won't Fix, 39,
3%
Postponed, 6,
0%
Results
Observations
Future Planning P
40,000 screens included:
62% (24,679) reviews as OK
Scope:
4,000 screens at once is
large and restricts achieving
100% coverage for all
languages
Smaller, more frequent
games
• Starting earlier in the
development cycle
• Focusing on new
functionality
• Alignment with Technical
Preview releases
• Broader Partner
participation
• Additional Language
coverage
P
18% (7,138) Not Reviewed
20% (7,833) O feedback
logged
1,643 Translation bugs
opened
Typical issues found:
• Context sensitive
translation
• Domain Terminology
• Un-translated Text
97% addressed (Fixed or
Dupe) before R2 Release
Coverage:
Partner participation in
some markets was low
Timing:
Running once , close to
‘EndGame’ leaves very little
time to address feedback
...and don’t run in August
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Microsoft Translator Data
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Recruit Translators and Select Translation Editor
Convert resources format to XLIFF with Microsoft conversion tool
Localizable XLIFF file is generated with resource strings
Translate the XLIFF in MAT Multilingual Editor or send it to the
localization vendor for professional translation through CAT tools
Convert translated XLIFF back to original resource format with
Microsoft conversion tool
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Microsoft
TM and
Materials from
the previous
releases for
training
Create a language
specific Project in
MT Hub
Create a new
System in the
project
and Train/Tune with
existing material
Deployment: Deploy
the System
Deployment: Deploy
the system
Partners
Additional
Training/Tuning
necessary?
Yes
Train new system.
Use accepted
reviewed text as
tuning data
(Optional) Known
good parallel data
from previous
releases for
training/tuning
Create a new system
by cloning the
current system
No
Localized material
Translation:
Machine Translate
the source material
Review: Have the
translation reviewed
by the assigned
reviewer
Finalize: Moderator accepts/
rejects the review results and
finalize the translation
Future implementation
Source material
Reviewer input
Localized material
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