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Translation in the 21 st Century

Explore the evolution of translation in the 21st century, from the complexities of language to the rise of machine translation. Delve into the impact of data sharing, hybrid systems, and the future of the translation industry. Learn about industry drivers, such as content explosion and real-time demand. Consider the implications of open collaboration versus closed competition and the integration of human and machine translation. Discover how events may shape the industry towards open collaboration, closed machines, or other scenarios, driving innovation and change in the dynamic world of translation.

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Translation in the 21 st Century

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  1. Translation in the 21st Century

  2. January 1954

  3. Tower of Babel Divide & Unite Language as Instinct Translation is very difficult …

  4. October 2004 Reality Check … TAUS founded.

  5. Let a Thousand MT Systems Bloom! Effectiveness of Data Profit from sharing (“Pirate’s Dilemma”)

  6. MT is here to stay Translation as a Utility … as a Human Right Five years ahead …

  7. Enterprises in 5 Years Need a Language Strategy not just reducing word rates “Dual Linguaspheres” Automate Innovate Collaborate

  8. Technology in 5 Years No Real Breakthroughs Hybrid Systems Targeted Correction (communities, games) Real-time Training

  9. TMs in 5 Years Cleaning Semantic Clustering Corpus Linguistics Preserve Endangered Languages Exciting new perspectives …

  10. Profession in 5 Years Choices End to Repetitive Tasks Productivity 5 to 10 Times Higher Supplemented by Non-Professional Volunteers

  11. Applications in 5 Years Translation out of the Wall Virtuous Circle Spoken Translation

  12. Industry in 5 Years Machines New Business Models ? Shared Resources & Utilities Growth Open (Collaborative) Closed (Competitive) Innovation comes from outside It’s all about the data Helping the world communicating better … Human & Machine

  13. Scenario-Based Planning • Adobe • Autodesk • Bowne • CA Technologies • Cisco • Dell • eBay • EMC • Google • Intel • Lionbridge • McAfee • Microsoft • Oracle • Paypal • Philips • SDL • Siemens • Symantec • Yahoo!

  14. Thinking about Drivers/Trends(notes from the Copenhagen meeting) Certain • Content explosion • Shift from text to text and multi-media • Mobile user, hand held devices • Real time/Just in time demand • Balance of cost, timeliness and quality Uncertain • Cloud translation replaces LSPs • Open (collaborative) Vs Closed (competitive)? • Fee Vs free? • Human Vs Machine? (incremental step or Technology breakthrough)

  15. Industry in 5 Years Machines Capability axe Collaboration axe ? Open (Collaborative) Closed (Competitive) Human & Machine

  16. Industry in 5 Years – Open/Machine Machines New Hope (free light saberon applications) ? Open (Collaborative) Closed (Competitive) Human & Machine

  17. Events that would Drive the Industry to the Open/Machine Quadrant • Pricing pressure from buyers are driving to a higher degree of automation • Transparency in collaborative environments supports benchmarking and thus competition • Standardization of measurements and quality levels provides comparability • Demand outpaces supply • Increased global competition of LSPs • Volume and price pressure forces LSPs to source globally, which is only possible using open standards • Buyers less concerned about competing with their peers.

  18. Characteristics of the Open/Machine Quadrant • Openness increases available resources • Cheaper per word than today • Cost reduction through a normalized and standardized pipeline • Shared TMs • Shared and standardized interfaces increase degree of automation • Standardized measurements are available to all

  19. Implications to the Translation Industry • Move away from per-word pricing to compensation for value-add activities • LSPs pick focus areas other than translating words.

  20. Characteristics Closed/Machines Machines The Dishwasher! ? Open (Collaborative) Closed (Competitive) Human & Machine

  21. Events that would Drive the Industry to the Closed/Machines Quadrant • If for any reason, open source fails • New, proprietary, much better private MT, patented, disruptive…. • This is where industry momentum is headed • Content mix shifts

  22. Implications to the Translation Industry • Lost opportunity for market • Seller’s market – cost stays high • Less richness of languages/localization • Less global availability • Less innovation

  23. Characteristics Closed/Human-Machines Machines ? Open (Collaborative) Closed (Competitive) Status Quo Human & Machine

  24. Events that would Drive the Industry to this Closed/Human Quadrant • Increased government regulation • Failure of MT to improve quality • Technology makes humans more efficient • Move to SAAS keeps control on IT costs • Sues Google and wins, locking content IT • Consolidation in the industry leads to stagnation • Increasing litigious society makes human checking essential • Maximizing shareholder value implies closed markets

  25. Key Characteristics of theClosed/Human Quadrant • Majority of “translation editing” is done with humans • Supply chain localisation stays with a few publishers • Only junk content gets MT’d • Crowd sourcing turns into email-spam

  26. Implications to the Translation Industry • No disruptive changes • Breeds complacency • Someone else will invent it for us • Clever people go elsewhere • Cloud and community stay around • People pay for software • Only first world to translation services

  27. Open/Human-Machine Machines ? Open (Collaborative) Closed (Competitive) Cyborg Cloud Human & Machine

  28. Events that would Drive the Industry to the Open-Human/Machines Quadrant • Geo-political • Availability of social networking (for example, PRC) • Accessibility to the technology • Online democratization • Content value / quality target • Human = high value/high quality, MT = lower value / lower quality • Post edit versus raw • Technology plateau • Limits of performance and quality • Legal requirement for human translation and quality • Drive to multimedia

  29. Key Characteristics of the Open/Machine Quadrant • Economic conditions drive to Open/Human • For example, recession • Slower technology evolution • Need to translate between a greater number of languages (for example Swahili and Brazilian Portuguese)

  30. Implications to the Translation Industry • More focus on standards • Cloud providers and social providers will play a greatly role • Opportunity to develop new tools for the human / machine translation interface • Cyborg • Voice-to-text

  31. in 5 Years Industry Think Tank Open Translation Platforms Language Business Innovation Industry Collaboration Translation Automation www.translationautomation.com

  32. in 5 Years Industry Data Resources Billions of Words Translation Matching TM Cleaning Matching Scores www.tausdata.org

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