1. The LSPs and Machine Translation: Why Not Treat MT as TM? David Canek, MemSource Technologies Torben Dahl Jensen, Oversætterhuset

2. MemSource Technologies Offshoot of a Charles University research project started in 2006 with Sun Microsystems Develops Translation and Authoring Software: – MemSource Translation Server – MemSource Translation Cloud – UTMA Authoring Server Headquartered in Prague

3. Oversætterhuset / Translation House of Scandinavia Leading Danish LSP with offices in Århus, Copenhagen and Kolding Established in 1990 Covers major European languages Eager to explore new technologies to make the translation workflow more efficient

4. Background The last LocWorld conference in Seattle covered MT deployments in Adobe, Autodesk and Cisco Last year’s LocWorld in Berlin also covered primarily enterprise case studies on MT What about LSPs and Machine Translation?

5. We Will Explore MT deployment scenarios MT quality assessment & monetization

6. MT ADOPTION

7. Enterprises? LSPs?Translators? Who Got MT Technology First?

8. Enterprises? LSPs?Translators? 1st: Translators Who Got MT Technology First?

9. Who Gets the Latest Technology First?

10. Translators and MT MT Deployment: easy – uploading files to Google Translate costs just a little bit of time; and it is free MT Monetization: trivial – MT simply speeds up their translations, so translators get more work done in less time

11. Enterprises and MT MT Deployment: challenging – but have the resources to manage this MT Monetization: complex – but being on the top of the food chain they have the power to renegotiate rates and drive home the MT- generated savings

12. LSPs and MT MT Deployment: challenging – have limited resources and specific obstacles MT Monetization: complex – will have to renegotiate translator rates to reflect MT savings

13. MT Deployment in an LSP

14. LSP Custom MT Development Considerable time and money to develop custom MT engine Can easily end up with MT quality far inferior than the free online MT services Specific obstacles: multiple domains and language pairs Google spent millions of USD, has excess of 100 billion words of training data...

15. A Scenario to Avoid LSP asks translator to post-edit a text machine translated by the LSP’s MT engine. The quality is poor. Translator deletes the machine translation and instead uses GT, gets much better results... On what basis can the LSP ask the translator to charge a reduced rate?

16. Can LSPs Succeed with MT? Yes. But do not necessarily start by developing a custom MT engine. Instead: Begin using a readily available MT service Measure its benefits See if/how you are able to monetize the benefits Only then explore the MT technology options

17. BUSINESS CASE

18. Building a Business Case for MT (MT savings) minus (MT costs) = MT Profit

19. MT Quality Measurement Today Kirti Vashee

20. MemSource MT Quality Measurement Simple, fast, precise Extends the established translation memory analysis and discount schemes to machine translation Why not treat MT just as another TM?

21. How Does It Work Exactly? Traditional translation memory analysis – Document source segment vs. TM source segment MemSource machine translation analysis – Document target segment vs. MT target segment

22. Translation Memory Match SourceTarget Europarat TM MT

23. Translation Memory Match SourceTarget Europarat TMEuroparatCouncil of Europe MT

24. Translation Memory Match SourceTarget Europarat TMEuroparatCouncil of Europe MT 100%

25. Translation Memory Match SourceTarget EuroparatCouncil of Europe TMEuroparatCouncil of Europe MT 100%

26. Machine Translation Match SourceTarget Europarat TM MT

27. Machine Translation Match SourceTarget Europarat TM MTEuroparatCouncil of Europe

28. Machine Translation Match SourceTarget Europarat TM MTEuroparatCouncil of Europe 100% ?

29. Machine Translation Match SourceTarget EuroparatCouncil of Europe TM MTEuroparatCouncil of Europe SourceTarget Europarat TM MTEuroparatCouncil of Europe 100% ?

30. Machine Translation Match SourceTarget EuroparatCouncil of Europe TM MTEuroparatCouncil of Europe SourceTarget Europarat TM MTEuroparatCouncil of Europe 100% ✓ 100% ✓

31. Analyzing MT Matches Simply analyze MT matches and add them to the existing TM matches:

32. Analyzing MT Matches Simply analyze MT matches and add them to the existing TM matches:

33. Analyzing MT Matches Simply analyze MT matches and add them to the existing TM matches:

34. Turning MT Matches into Money Use your own discount scheme, e.g.: TM Match % of Rate Paid New words100% 75%-84%50% 85%-94%33% 95%-99%25% 100%10%

35. Turning MT Matches into Money...and add MT matches TM & MT Matches % of Rate Paid New words100% 75%-84%50% 85%-94%33% 95%-99%25% 100%10%

36. Knowing Your MT Savings When you know your MT savings, you can also better decide how much you can afford to pay for the MT service/technology.

37. CASE STUDY RESULTS

38. Case Study Overview Two LSPs participated January – May 2011 Domains: – Marketing – Law – EU – Technology

39. Case Study Overview Language pairs: – English > Danish – English > Norwegian – English > Czech – Czech > English – English > German Volume: 1 million words Two MT engines: GT and a custom MT engine

40. Case Study Results: Domain MT Match RateLegalTechnology 0%-50%73%38% 50%-74%17%21% 75%-84%5%10% 85%-94%2%14% 95%-99%1%6% 100%2%11%

41. Case Study Results: Language MT Match RateEN>CS EN>DE 0%-50%72%62% 50%-74%14%19% 75%-84%6%8% 85%-94%3%4% 95%-99%2% 100%3%5%

42. Case Study Results: LSPs MT Match RateLSP1 LSP2 0%-50%69%63% 50%-74%15%18% 75%-84%7%6% 85%-94%4%5% 95%-99%2%3% 100%3%5%

43. Next Steps Talking to translators and post-editors about the new approach Negotiating TM/MT based discount schemes...

44. THANK YOU