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Translation in the 21st Century Impacts of MT and social media on language services January 1954 Tower of Babel Divide & Unite Language as Instinct Translation is very difficult … October 2004 Reality Check … TAUS founded. Let a Thousand MT Systems Bloom! Effectiveness of Data Profit from sharing (“Pirate’s Dilemma”) MT is here to stay Translation as a Utility … as a Human Right Five years ahead … Technology in 5 Years Hybrid Systems Targeted Correction (communities, games) Real-time Training TMs in 5 Years ownership? Cleaning Semantic Clustering Corpus Linguistics Preserve Endangered Languages Exciting new perspectives … Profession in 5 Years Choices End to Repetitive Tasks Productivity 5 to 10 Times Higher Supplemented by Non-Professional Volunteers Applications in 5 Years Translation out of the Wall Virtuous Circle Spoken Translation Industry in 5 Years Thinking about drivers/trends From TAUS Copenhagen Forum (May 2010) Certain Explosion in new content Shift from text to text and multi-media (word counts go down) Mobile user, hand held devices Real time/Just in time demand Cross-lingual translation challenges Balance of cost, timeliness and quality Uncertain Open (collaborative) vs Closed (competitive)? Fee vs free? Human vs Machine? (incremental step or technology breakthrough) Industry in 5 Years Machines SWOT Content disruption Openassessment Data (Collaborative) Innovation dilemma Embedding technology ? Human & Machine Closed (Competitive) SWOT for Enterprise Language Service S W • High leverage from TM • Well established process and management O • Opening new markets with MT • Engaging with users & communities • Convergence with video and speech • Search engine optimization • Translation of user generated content • Quality inconsistent (local flavor missing) • Lack of flexibility, reactive rather than creative T • Rigid landscape (vendor lock-in) • Not scalable to expand quickly • Inability to ensure quality in new markets • Lack of corporate awareness of new locales Content Disruption Sales Localization industry Web “Battle for words” UI Manuals Support Social media Knowledge Base User generated content New technologies and solutions Innovation Dilemma S • High leverage from TM • Well established process and management O • • • • • Opening new markets with MT Community/user feedback Convergence with video and speech Search engine optimization Translation of user generated content • Quality inconsistent (local flavor missing) • Lack of flexibility (reactive, rather than creative) W T • Rigid landscape (vendor lock-in) • Not scalable to quickly support new markets • Inability to ensure quality in new markets • Lack of corporate awareness of new locales Innovation Dilemma S• High leverage from TM • Well established process and management O • • • • • Opening new markets with MT Community/user feedback Convergence with video and speech Search engine optimization Translation of user generated content • Quality inconsistent (local W flavor missing) • Lack of flexibility (reactive, rather than creative) T • Rigid landscape (vendor lock-in) • Not scalable to quickly support new markets • Inability to ensure quality in new markets • Lack of corporate awareness of new locales Business Model Attributes Old Model 1. One translation fits all 5. Word-based pricing 2. Project-based translation 6. GMS system 3.TM is core 7. Cascaded supply chain 4. One-directional 8.Translate-Edit-Proof New Model 1. Quality differentiation 5. SaaS – Value-add 2. Continuous translation 6. MT embedded 3. Data is core 7. Community – user 4. Multi-directional 8. Post-edit – Real-time – Peer review Enterprises in 5 Years Need a Language Strategy not just reducing word rates “Dual Linguaspheres” Enterprises in 5 Years Machines MT embedded. Data is core. Open Continuous translation. (Collaborative) Community/user. SaaS + Value-add. ? Project-based. Cascaded supply chain. Closed (Competitive) Word-based pricing (text). GMS workflow systems. TM is core. Human & Machine Strategic Roadmap Machines MT utility SaaS + Value-add. Closed (Competitive) Project-based. Data is core. Continuous translation. Cascaded supply chain. Word-based pricing (text). Quality differentiation One translation fits all Community/user. TM is core. 2011 2012 2013 Human & Machine Open (Collaborative) Language Data Management The Power of Data for Translation 1. 2. 3. 4. Terminology mining and dictionary building Customize automated translation Global market and customer analysis Quality management in 5 Years Industry Think Tank & Innovation Partner Research Consulting Events - workshops www.translationautomation.com in 5 Years Industry Data Resources Billions of Words Translation Matching TM Cleaning Matching Scores www.tausdata.org