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The LSPs and Machine Translation: Why Not Treat MT as TM?

The LSPs and Machine Translation: Why Not Treat MT as TM?. David Canek, MemSource Technologies Torben Dahl Jensen, Oversætterhuset. MemSource Technologies. Offshoot of a Charles University research project started in 2006 with Sun Microsystems Develops Translation and Authoring Software:

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The LSPs and Machine Translation: Why Not Treat MT as TM?

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

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

  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

  23. Translation Memory Match

  24. Translation Memory Match 100%

  25. Translation Memory Match 100%

  26. Machine Translation Match

  27. Machine Translation Match

  28. Machine Translation Match 100%?

  29. Machine Translation Match 100%?

  30. Machine Translation Match 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.:

  35. Turning MT Matches into Money ...and add MT matches

  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

  41. Case Study Results: Language

  42. Case Study Results: LSPs

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

  44. THANK YOU

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