1. Natural Language
Processing for Translation
Alessandro Cattelan, Translated srl

2. Language industry size
Language service industry
Extremely fragmented
$33.5 billion
market both in terms of
in 2012
http://www.commonsenseadvisory.com/Portals/0/downloads/12
0531_QT_Top_100_LSPs.pdf
language service
providers and customers.

3. Language industry customers
Large customers spend millions
of dollars a year in translation.
However, it is the smaller
customers with limited budgets
that make up most of the market.

4. Specific characteristics
Larger customers
 Large budgets
 Use technology (MT, TM, termbases, etc.)
 Efficient processes (translation is part of the development
cycle)
Smaller customers
 Tight budgets
 No technology and no processes

5. Smaller Customers
Even though they are on a
tight budget and use no
technology for translation, we
can still give them something
better than this…

6. Common requirements
Both smaller and larger customers are interested in:
 Getting high quality translations
 Receiving the translation as soon as possible
 Saving as much as possible

7. Challenge → Opportunity
No technology
and no processes
to improve
efficiency in
translation
Develop
technology and
processes to win
customers

8. Content reuse
Translation
Memory
Large public translation memories
make it possible to leverage
previously translated content and to
reduce weighted word count.
 Collecting data
 Aligning bilingual content
 Making data available in CAT tools

9. Translation Memory
Never translate the same sentence twice… nor part of it!
 Improving matching algorithm for translation memories
EN
IT
To open a file, select File from the
menu and click on Open
Per aprire un file, selezionare File
dal menu e fare clic su Apri
Select File from the menu
[…]

10. Translation Memory
Never translate the same sentence twice… nor part of it!
 Improving matching algorithm for translation memories
 Using MT to complete fuzzy matches
EN
IT
Select File from the menu
Selezionare File dal menu
Select File from the menu and
click on New document
Selezionare File dal menu […]

11. Machine Translation
Most of the times, customers do not have custom MT engines nor
the data to create an engine.
 Use existing domain-specific engines, even though they are not
adapted to the customer
 Adapt generic engines to specific domains (needs to be fast!)
 Adapt the engine in real-time with the user translations

12. Using generic engines
“If I have seen further it is by standing on the shoulders of giants.”
[I. Newton]
Post-processing of MT output from generic engines:
 Correcting terminology issues
 Adapting output to previous translations
 Managing mark-up…

13. MT quality evaluation
Establishing the right weight for words translated by MT systems.

14. MT quality evaluation
What is a fair rate
for editing machine
translation output?
 Confidence scores for MT
 Matching metrics for TM
segments
 MT quality perceived by the
user

15. Terminology Management
Terminology management can have a great impact on
quality and productivity.
 Automatic extraction of terminology
 Finding target language equivalents for source terms
 Adding context to the terms

16. Any questions?