PayPal Translation Model Catherine Dove, Linguistic Manager October 7,

Agenda Introduction – Priorities and cycles 7 years ago… Language top issues Today in review Our Vision 2

About PayPal Leading global online payment company, founded in million active registered users (210 million total accounts) Available in 190 markets 20 websites in local languages 2009 annual revenue was $2.8B 3

priorities Time to market Support Release Acceleration - more products, more often… continuing simship Customer Quality Complete end-to-end quality for our Localized sites 4

Time to market – Bi-weekly release 5 Dev / Shakedown FQAIntegrationRQAThrottleLTS Pull Forecast Drop 2 Drop 1 LQA Testing Sanity Check BWR (6 weeks) Pull new feature content BWR L10N involvement lasts 17 days Includes Marketing pushes every 7 days Includes a number of unplanned releases

24x7 L10N Eng/LQA support 6

Who & What’s Involved in an L10N Drop? 7 Vendors translate PayPal linguists review Linguists fix bugs L10N engineers fix bugs Dev fixes bugs Developer checks in changes L10N processes, sends to translation LQA tests, files bugs to L10N and Dev LQA builds, deploys to test stage L10N processes, delivers to ClearCase

7 YEARS AGO… 8

Language top issues No context during translation No translation validation tools Archaic translation memory setup Internationalization as an after-thought Lack of controlled source language 9

No context during translation… No file preview available Vendors and linguists translated in the dark 10

… or translation validation tools The translation effort (i.e. damaging code or HTML tags) can break functionality, yet there was no automated way to detect most of the problems prior to Localization QA On staging, English text meant either a bug (hard-coded text), or a source change since the last translation drop, and you could not tell the difference between the two 11

Archaic translation memory setup 1.5 Million words translated per language… without multiple translation support, or even less, real time leveraging Different file formats for each component required separate translation memories… And there was no TM server, or centralized database for translated assets and translation memories 12

You said i18N? Internationalization (i18N, or the process of creating a product flexible enough to be easily adapted to different languages/countries across the world) was an after-thought - No centralized i18N function at PayPal o Many teams did their own i18N and involved L10N late in the process, or were not even aware of i18N and L10N best practices - By the time internationalization issues were detected, it was too late to have them fixed in the current release 13

Content, Content, Content… Lack of controlled English and terminology management in source content created a lot of churn Jargon (including internal terms such as “backup funding source”, “Marketplaces”, “batch processing”) made the English text difficult for translators… and customers to understand Too many slightly different versions of the same string increased L10N workload and costs 14

TODAY IN REVIEW 15

In-context rendering during translation DXPT/Idiom integration On-demand page rendering through the PayPal Idiom Workbench web application When a translator requests an on-demand page render, the Idiom web application retrieves the page and renders the content in the translator’s browser Static Page clicker Available for the rendering of PayPal s 16

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Confidential and Proprietary19 Static Page clicker ( s) Real-time leveraging Real time translation memory reconciliation Translation memory groupings to reference other components during translation - -[Graphic for Maintenance process]

Confidential and Proprietary20 L10N QA Highlighting  Blue highlight New or changed English text (typically the result of bug fixes in WebDev or Content in the en_US Source) which has not been translated yet. Allows to detect hard-coded text and avoids filing unnecessary bugs.  Yellow highlight This is new or changed English text which was translated in the last translation delta. This text is the primary focus of PayPal’s internal review.  Green highlight This is new or changed English text that was 100% leveraged by our translation memory in the last translation delta. This text was previously translated in a potentially different context. PayPal needs to focus on this as well, but possibly less critical.  Purple highlight This is used to highlight text which was written directly in the local language and did not go through translation.

Confidential and Proprietary21 This English text is NOT hard-coded

Confidential and Proprietary22 This German text - recently translated - requires LQA

Confidential and Proprietary23 World-class translation memory setup Real-time leveraging One translator’s output is instantly leveraged by another, reducing translation turn-around times Real time translation memory reconciliation Working TM is instantly updated Translation memory groupings allow to reference other components during translation TM hierarchy maximizes leveraging while ensuring the best possible in-context match

Confidential and Proprietary24 Automated validation for translated assets Validation step allows to detect well-formed or HTML errors, enabling real time fixes in the Idiom workflow and reducing workload of engineers and linguists

“ Gold” translator/post-editor model Test all of our vendors’ individual translators and only keep the best Increase quality and decrease turn-around time by having dedicated vendor resources who know PayPal in and out Potentially reduce vendor TEP process from 3 rounds to 1 round (manual translation), or 2 to 1 (machine translation) Set up back-up system 25

Internationalization strategy Creation of an Internationalization team to develop standards across the entire code base L12Y (Localizability) >>> evaluating UI designs, making sure the layout, and product language can be accommodated Pseudo-localization and machine translation allow the early detection of Localizability issues Automated scripts allow to detect specific types of errors prior to file delivery across the entire code base Example: hard-coded text Confidential and Proprietary26

27 Controlled language Check for forbidden terms Run linguistic and style checks Check against translation or authoring memory Central server Create source content (English or local language) Translate content Terminology Language rules Translation memory

Machine translation Why machine translation? MT improves the productivity and consistency of human translators It’s fast, customizable, predictable Approach Rule-based MT engine built for PayPal domain specific translation Currently in production RU, UK, IT, ES for certain components Soon migrating to hybrid engine Plans Test other systems and implement for more languages Use MT engine functionality for LQA and pseudo-localization 28

MT will not replace Human Translation (HT) 29

Human Translation vs. MT Workflow Idiom Untranslated segments Translator Translation Editing Linguist Review Final text Idiom+MT Pre-translated segments Post-editor Editing 30

Roadmap to quality MT output Improve with more customization Get feedback from post-editors Customize MT rules & dictionaries Simplify Source with Content and Acrolinx teams Evaluate MT Quality (Pilot 1) 31

OUR VISION 32

New model for content origination and localization Confidential and Proprietary33 Jargon free, non-colloquial English as pivot language for global content origination Improved L10N workflow Tools upgrade to improve time to market and quality Internal sanity checks for local fit LQA/UAT/BU focuses on Customer Exp and sign-off Approved language quality Live! – No surprises Gold translators translate in context Jargon free, non- colloquial, pivot English content

Pivot English for MT 34 English content authoring Machine translation MT rules customization Acrolinx rules customization Translation Acrolinx check English content editing 1 English content editing 2

Authoring in local language (20% of the content) 35 Local language content authoringAcrolinx check (integrated with Idiom)Local language content editing Translation into US English (as required)

Confidential and Proprietary36

MT on the fly 37

MT on the fly… from Dev to Support - Pivot English and L10N tools increase quality - Connectivity (Integrate content creation environment with L10N) - Accelerated throughput (Machine translation with post-editing in Real- time leveraging and TM reconciliation) Summary 38 L10N technology Faster time to market with higher language quality

39 Thank You! Q & A