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iadaatpa gala boston

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4 European machine translation companies joined forces to build something bigger than themselves: an intelligent platform capable of detecting domain, detecting languages, balancing load with a view to create a marketplace. The project was financed by the European Commission. This is the presentation by Pangeanic in Gala Boston 2018.

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iadaatpa gala boston

  1. 1. iADDATPA the Collaboration of Four MT Developers to build an open source MT platform Manuel Herranz, CEO
  2. 2. An investment in knowledge pays the highest interest Benjamin Franklin
  3. 3. Demonetization: «Technology allows for products and services which used to be expensive to become affordable or even free. “Free” appears in products which can be digitalized (turn atoms in bits): Google, Wikipedia,YouTube. » «Smartphones are cheaper and more powerful than 1970’s 80’s 90’s computers» Peter Diamantis: «Abundance»
  4. 4. Things can change overnight
  5. 5. Things can change overnight
  6. 6. Majored in mechanical engineering & languages (UK) Joined Giddings & Lewis - Ford Valencia / Chihuahua 1993 - 1996 Rolls Royce Marine & Industrial Spain / Argentina 1997-98 and 2000 Joined B.I Corporation Japan 1996-2004 Friendly buy-out 2005: Pangeanic A few words about…Manuel Herranz
  7. 7. Intelligent Automatic Domain Adapted Automated Translation for Public Administrations First time 4 MT companies join forces to build something greater than them EU CEF infrastructure - contract Open source Single Access Point What is iADAATPA?
  8. 8. Intelligent Automatic Domain Adapted Automated Translation for Public Administrations Public administrations from one country will be able to use FREE (yes, FREE!!!) machine translation when dealing with each other. Documents, panel, etc. Integrated in other services and building blocks What is iADAATPA?
  9. 9. Intelligent Automatic Domain Adapted Automated Translation for Public Administrations Compatibility with several CAT tools as requested by PPAA What is iADAATPA?
  10. 10. - AT systems receive webhook - Ask for “content request” CMS 2 CMS 1Tilde MT Pangeanic KantanMT AT systems IADAAPTA Platform (cloud vs on-premise) CKAN Widget browser eTranslation - Requests: Supporting both synchronous and asynchronous requests - Many IADAAPTA deployments are possible. - A global instance register is kept by commercial partner. - send translation request - receive webhook - Ask for “request done” Priority Admin Lang / Q router User management/ Profile BACKOFFICE: - Global (instance management) - Individual (for each instance) Documents (proprietary formats) Conversion e-Sens AS4 Profile Complia nt) e-Sens AS4 Profile Complia nt) Prompsit How will it work?
  11. 11. Intelligent Automatic Domain Adapted Automated Translation for Public Administrations What is iADAATPA? Work in progress Website translation plugin for private viewing (intelligence services or incognito)
  12. 12. Intelligent Automatic Domain Adapted Automated Translation for Public Administrations What is iADAATPA? Work in progress Website translation plugin for private viewing (intelligence services or incognito)
  13. 13. An investment in knowledge pays the highest interest Demonetization New values and services?
  14. 14. This is more realistic: MT in the wild, wild, wild world Quite an accurate workflow when integrating MT at a company MT engine Wouldn’t it nice to go from Quite an accurate workflow when integrating MT at a company
  15. 15. Fight in a segmented market Enable international business Help people /organizations to communicate Innovate Differentiate? At what speed? Really? What we do as an industry… Can we process 400M words in a week/days?
  16. 16. Disintermediation Companies that re-invented business models Or offer added value/extras? LSPs to become - language recruitment agencies - HHRR specialists? - tech integrators? Is our business model viable in 5 years?
  17. 17. 95% LSCs have no iSEO strategy (beyond having a bilingual website) beca translation is expensive. Do you invest in the product you sell? 80% have no national SEO strategy 50% apply /adopt MT (only 25% have MT embedded in their systems / custom engines) 10% have centralized TM system to leverage past content. Most operate with hundreds of TMs in a server. Are we ahead of the game? Losing out in 1-1 internet revenue Losing out in speed
  18. 18. Industry revenues more than doubled from ~$19B in 2005 to ~$40B in 2016. [Common Sense Advisory data] Translation buyers worry about ever-growing content volumes and more language pairs – but with stable or shrinking budgets. Management expects Amazon, Microsoft or Google Translate will take care of “language problems” one day less complexity, lower cost. The US Census shows that translation workers doubled since 2008. [Slator, May 24, 2017] Automation: project management value replaced by bots? The number of working translators on LinkedIn has increased by 50+% since 2010 [LinkedIn data] Market forces squeeze mid-sized companies from both ends: large can offer economies of scale. Small are specialists, niche or local. Business model in 5 years, 4 years, 3 years
  19. 19. When Google was founded in September 1998, it was serving ten thousand search queries per day (by the end of 2006 that same amount would be served in a single second). Business model in 5 years, 4 years, 3 years iADAATPA is about Empowerment Disintermediation / Direct clients Satisfying the 5Bn searches/day and increasing demand for cheaper language services. Really not your market? TM+MT leveraging, CAT agnostic, inexpensive tools
  20. 20. Business model in 5 years, 4 years, 3 years Best of class MT / Marketplace Granular combination Any CAT tool (if they exist) Documents Websites Speech-to-Text
  21. 21. Tests in F/I/G/S, RU, PT point to a very strong preference towards NMT fluency bit.ly/neural-machine-translation-pangeanic. On average: from a set of 250 sentences, around 85%-92% were good or very good (A or B). ES/PT/IT results similar to FR Evaluation: Translation companies and professional freelance translators EN-DE set of 250 sentences NMT SMT A 132 53% 34 14% B 98 39% 95 38% C 14 6% 97 39% D 6 2% 24 10% 250 250 EN-FR set of 250 sentences NMT SMT A 150 60% 39 16% B 76 30% 126 50% C 21 71 28% D 3 14 6% EN-RU set of 250 sentences NMT SMT A 128 51% 39 16% B 84 34% 43 17% C 22 9% 60 24% D 16 6% 108 43% 250 250 A crash course into neural MT
  22. 22. Class “CAT” is selected as it got the highest value Neural Machine Translation
  23. 23. Training set Test Reference translation Out of which we take 2000 sentences to try the system with in- domain text (a typical sentence the system may encounter in the future) Remove any protocol configuration files that are not used for the specified protocol . These tables are sometimes referred to as " no sync " tables . This chapter will describe many of those pages and parameters . Feed Forward Neural Machine Translation
  24. 24. Error function (detects “wrong match”) Input Query Label (data we already know) Output Update function, ie (the “learning process”) New Weights (W) + New Bias (B) And after many, many training sessions detecting patterns, trial and errors and feedback loops… Feed Forward Neural Machine Translation
  25. 25. Label (data we already know) Output Error function (detects “wrong match”) Input Query No Update !!! (“learning process” completed) Now we have a system!!!! Input Queries Output Labels 80%-85% accuracy!! Feed Forward Neural Machine Translation
  26. 26. Attention models tell the system which encoder states to look at a good and sound agreement un buen y sólido acuerdo un buen y sólido acuerdo <s> <s> Recurrent NMT + Attention Models
  27. 27. Are we working in the same way as 5 years ago? Will we be working in the same way in 2023? Translation companies to provide translation services only? New business models: offer translation order automation (management systems), disintermediation, raw doc+ MT services? Will large translation companies consolidate and dominate globally? Can new players emerge with the right tools, selling globally? Is translation company-to-translation company selling a viable model? Open Questions
  28. 28. Thank you! Manuel Herranz m.herranz@pangeanic.com / m.herranz@pangeamt.com Twitter: manuelhrrnz

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