10 Decisions to make before starting to use Machine Translation (MT), including details on how to improve MT engines.

1. Your Trained Moses SMT
System doesn't work.
What can you do?
Diego Bartolome, CEO tauyou <language technology>
diego.bartolome@tauyou.com
@diegobartolome

2. Where are you now?

3. Where are you now?

4. Why Machine Translation?
Strategic decision
Increase sales
Shorten delivery times
Reduce costs
Differentiation
Forced decision
Clients ask for it!

5. Dare – change
5

6. Welcome to the jungle

7. Decision 1: Internal – external
Core competence
Resources
ROI
Time to market

8. Decision 1: Internal – external
Core competence
Resources
ROI
Time to market

9. MT Costs
Internal development
Free tools
DOiY solutions
Traditional pricing model
tauyou managed solution

10. Decision 2: MT Type (I)
Rule-based MT
Statistical MT
Hybrid MT

11. Decision 2: MT Type (II)
Do we really care?

12. Decision 3: Languages (I)
Source: translate.autodesk.com

13. Decision 3: Languages (II)
Source: Philipp Koehn

14. Decision 4: Domains
Who is willing to pay?
Where does your revenue come from?
What are your key skills?
What domains achieve good quality?

15. Decision 5: Workflow
Use MT as a secondary TM
Bilingual pre-translated translation files
CAT tool integration
Differentiated workflow

16. Decision 6: Feedback
Qualitative
Use updated TMs in new trainings
Immediate (incremental) retraining
Rule-based automatic post-editing
Selective pre- and/or post-processing

17. Decision 7: Post-editors
What are the skills needed?
Post-editing guidelines
How do we pay them?

18. Decision 8: Metrics
SMT metrics: BLEU, NIST
Feedback from translators
Translation time vs. Post-editing time
Word Error Rate (WER) or Edit Distance
Cost reduction

19. Decision 9: Business Model

21. Decision 10: Start!

22. Let's play with Moses

23. Let's play with Moses
Best resource to start
www.statmt.org/moses
TAUS tutorial
www.translationautomation.com
tauyou slides
www.speakerdeck.com/tauyoucom

24. Everything is clear!
Gather TMs and other linguistic assets
Select domains
Train systems
BLEU score is great
… but …
Translation quality is awful

25. Why?
Not enough data
Too much data
Unclean TMs
Misalignments
Difficult language pairs
Selection of wrong parameters
Suboptimal techniques

27. Some steps
Maximum exploitation of existing assets
Source content optimization
Data selection and cleaning
Improvement of the models
Linguistic processing
Continuous improvement

28. Linguistic assets
Translation memory sharing
Clients, Partners, EU, UN, TAUS
Relevant on-line data retrieval
Advanced TM techniques
Sub-segment matching
Parts of Speech replacement

29. Source optimization (I)
Spell check
Grammar check
Style check
Terminology check
Client checklist
new
doc
proposed
doc + html
report

30. Summarization
% to reduce
Use translation memories
Project
Client
All
new
doc
proposed
doc + html
report

31. Data selection + cleaning
Clean translation memories
Length, punctuation, terminology, …
Inconsistencies, repetitions, ...
Segment splitting
Optimize weight of most frequent n-grams
Validate their translations
Add out-of-domain data

32. Models optimization
Filter the translation tables
Remove the garbage + tune weights
Optimize language models
Adapt them to the translation purpose
Tune parameters correctly
Tune set, test set, optimization parameters
Improve recasing

33. Linguistic processing
In the source and/or target language
Grammar checking
Entities detection
Proper nouns, alphanumeric words, ...
Compound words splitting
Sentence reordering

34. Life is about the people you meet and
the things you create with them.
So go out and start creating
Part of the Holstee Manifesto
Diego Bartolome
CEO tauyou <language technology>
diego.bartolome@tauyou.com
@diegobartolome

35. Thank you!
Diego Bartolome
CEO tauyou <language technology>
diego.bartolome@tauyou.com
@diegobartolome