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What you need to put 
Machine Translation 
into practice: 
Tools, People, and Processes 
Diego Bartolome 
@diegobartolome 
dbc@tauyou.com
60+ clients 
16 countries 
~1 billion words in 2013 (cost reduction) 
All language pairs
Why Machine Translation?
Why Machine Translation? 
Price pressure 
Tight deadlines 
Client requests (impossible projects) 
New needs from clients 
Everyone else is doing it 
...
Machine Translation requires 
Initial investment 
Time 
Continuous improvement 
Change in company culture 
Clear goal
Machine Translation Companies 
and others
What do you look for?
Some ideas 
Confidentiality Customization 
Control User friendliness 
Flexibility Technology 
Business model Quality 
Relationship structure Trust
Engine building FAQ
Engine building FAQ 
How many words/segments do I need? 
How do I measure quality? 
What language pairs work best? 
What are the solutions if quality is unacceptable? 
How often engines need retraining?
Process integration 
What is the best process?
Most extended process 
Prepare files 
Pre-translate (>85%) 
Prefill remaining segments with MT 
Post-edit MT + review TM 
Update TM (+MT) + compute quality metrics 
Automatic Post-editing Rules
People 
What are the challenges you have faced?
People 
Averse to change 
Importance of less time consuming errors 
Training 
Age/years of experience 
Time vs. money
3 hints 
Tools 
invest in choosing the right one for you 
Process 
decide a process from the beginning 
People 
make everybody win
Post-editing examples
Change before 
you have to 
Jack Welch
Other interesting topics 
Optimized big data approach to MT 
Quality Checks 
Online postediting interface 
Process automation (API)

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What you need to put Machine Translation into practice: Tools, People, and Processes