What Data would you like to Track? - Achim Ruopp
In all of our translation production activities we are producing data, lots of data. We are not talking now about the actual translations that are stored as translation memory data. These translation memory data have proven to be very valuable over the years and recently again as training data for Machine Translation engines. But in this session we are talking about the other data: data about the translation process. How much time was spent on different tasks, for different languages, content types, per project? What was the quality score for the translator, for the vendor? What was the user feedback on this machine translated support article? How is our MT engine performing? And has it improved since last year, since we have added 13 million more words in the training set? Some of the buyers and providers of translation are further ahead with the use of all these translation management data than others. The TAUS Dynamic Quality Framework (DQF) tracks translation management data through plug-ins that are already available for various translation tools and platforms. The vision is becoming very clear: the translation industry can have its own “Big Data”. In the past couple of months TAUS enterprise members have contributed their wishes and requirements for an industry benchmarking platform for translation quality and productivity. In this session several TAUS members will share and discuss their plans for using DQF and the Quality Dashboard. What data would you like to track? Session host: Daniel Goldschmidt (Microsoft) Presenters and panelists are: Annya Sedakova-Bertram (EMC), Fred Tuinstra (Lionbridge), Achim Ruopp (TAUS)
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to What Data would you like to Track? - Achim Ruopp
Similar to What Data would you like to Track? - Achim Ruopp (20)
More from TAUS - The Language Data Network
More from TAUS - The Language Data Network (20)
Recently uploaded
Recently uploaded (20)
What Data would you like to Track? - Achim Ruopp
- 1. Industry Shared Metrics with the TAUS Dynamic Quality Dashboard and API Achim Ruopp, Directory of R&D www.taus.net
- 2. What About Translation Quality Old School: “One size fits all” Since the 1980’s LISA QA Model, SAE J2450 prescribe today’s quality processes: 1. Static: • One quality fits all purposes, all content, all audiences 2. Subjective: • Evaluations are often subjective and anecdotal 3. Costly: • QE causes friction, delays • QE can cost up to 25% of total translation costs 4. Non-transparent: • Necessity without remedy Dynamic Quality Framework
- 3. Industry Collaborative Program DQF started in 2011 Adobe Appen Autodesk AVB CA Technologies Cisco Crestec Crosslang Dell DFKI eBay EMC Google Hewlett Packard Intel LDS Church Lingo24 This slide may not be used or copied without permission from TAUS Participating members Lionbridge Medtronic Microsoft Moravia Nikon Oracle Pactera Pangeanic Paypal Philips PTC Siemens Spil Games Systran VMware Welocalize Yahoo!
- 4. The TAUS Dynamic Quality Framework Content Profiling Select the most appropriate quality evaluation model for specific requirements Knowledge Base Find best practices, metrics, step-by-step guides, reference templates, and use cases DQF Tools Post-edit machine-translated segments to track productivity Evaluate adequacy and fluency Count errors based on an error-typology Rank machine translations
- 5. DQF Tools (Jan 2014) • Productivity • Adequacy • Fluency • Translation errors >> Stats and reports DQF + Quality Dashboard (June 2015) Translation tool integration New Reporting Features (real time) >> Enhanced statistics >> Industry averages >> Benchmarking Quality Evaluation [mainly MT] Quality Evaluation [ALL processes] + Business Intelligence Used by 100+ members
- 7. Data point v1.3 v3.0 v3.5 Language pair X X X Project X X X File X X Edit time per segment Time of edit X X X Segment origin data X Enhanced Enhanced Source segment Target segment Edited target segment Including history Optional Word counts Edit distance X Errors X X X Project/translator key X Supply chain workflow X X CAT internal identifiers X X Data Instrumentation Comparison
- 9. Reports in the Quality Dashboard Productivity per Language
- 10. Reports in the Quality Dashboard Productivity per Time
- 11. Reports in the Quality Dashboard Productivity per Technology
- 12. Reports in the Quality Dashboard Errors per Reviewer
- 13. Reports in the Quality Dashboard Errors per Project
- 14. Reports in the Quality Dashboard Errors per Severity Level
- 15. Reports in the Quality Dashboard Errors per Vendor
- 16. Reports in the Quality Dashboard Productivity per Reviewer
- 17. This slide may not be used or copied without permission from TAUS THANK YOU!