Evolution of Computer Terminology Translations with the SPOT Dictionary Jiri Hynek, Premek Brada Department of Computer Science & Engineering Faculty of Applied Sciences University of West Bohemia, Pilsen, Czech Republic

2 “On the quality of Czech translations of technical literature” Apply filters  Zažádej o filtry  Ask for filters  Why the ICT translator’s work is hard  What we can do to help alleviate it

3 Standard translation process  Translation, localization projects (books, software) (up to) millions of words in various contexts team work (export, import, corpus sync) need for consistency, efficiency, quality  dictionaries, corpora, CAT tools  Problems a translator runs into grasp meaning of highly specialized terms be aware of already existing term translations

4 Making term translations  Linguist, corpus, current dictionaries  Months, years of preparatory work and polishing  Published dictionary  ICT context: process works well for “stable old-timers” like memory, disk, dialog window

But what if new words appear every month, and get accepted worldwide within weeks?

6 Troublemakers among words  Old-timers  Novas and supernovas  Homonyms, synonyms  Shifting sands  Esoteric terms  … appear in books, papers, blogs, student works (diploma theses), documentation, software applications  meet us daily

7 Troublemakers (1)  Synonyms (pull-down | drop-down)  Common words in new meaning  Problems acceptance in non-original languages alternative translations – which is correct?

8 Troublemakers (2)  Supernovas, esoteric terms – spam, phishing, blog, code closure, social bookmarking, refactoring, proxy, locale, box model, marshal, bean-managed persistence  Problems “explanatory translations” instead of terms narrow community users prefer original to translation

9 Ways out  Translate only few books?  Educate translators?  Educate developers, L10N specialists? … or …

10 SPOT  Dictionary of (evolving, current, unsettled) ICT terminology translations  Goal = reconcile two forces terminology from users quality of resulting language

11 Collective wisdom approach  Community to create term translations  Fruitful cooperation of linguists, knowledgeable users, translators, and professionals (engineers, academics) complementary skills, contexts, knowledge  cf. COBUILD approach to capture language “as is” rather than “as should be”

12 Key community supporting features  Dictionary searching browsing (letter, category, project, status) links  Communication comments voting

13 Key professional support features  Editorial board translation quality  Translation projects isolation communication geographic distribution  Live term contexts Google search several context categories

14 SPOT as corpus building tool  Translation settling process user adds term user adds translation (with notes) registered users comment, vote editor decides  correct vs deprecated translations  Projects developing local corpus in isolation publishing for general availability

15 SPOT as dictionary and CAT  Dictionary with added value good/bad translations, comments, explanations context … as compared to FOLDOC; slovnik.zcu.cz  On-line CAT tool corpus sharing and updating, discussion draw from already existing translations … as compared to Google, reference.com, dictionary of math terminology, TU Brno

16 Conclusion  ICT translations challenging even for skilled professionals  SPOT: let translations become a shared work of the ICT “general public” Thank you