1. s andrejs vasiļjevs chairman of the board andrejs@tilde.com data is core LOCALIZATION WORLD PARIS, JUNE 5, 2012

2. Language technology developer Localization service provider Leadership in smaller languages Offices in Riga (Latvia), Tallinn (Estonia) and Vilnius (Lithuania) 135 employees Strong R&D team 9 PhDs and candidates

3. MT machine translation

4. INNOVATION disruptive

5. rule-based MT statistical MT High quality translation in specialized domains Require highly qualified linguists, researchers and software developers Time and resource consuming Difficult to evolve Translation and linguistic knowledge is derived from data Relatively easy and quick to develop Requires huge amounts of parallel and monolingual data Translation quality inconsistent and can differ dramatically from domain to domain MT paradigms

6. CHALLENGE

9. one size fits all ?

10. DATA

12. The total body of European Union law applicable in the EU Member States JRC-Acquis http://langtech.jrc.it/JRC-Acquis.html

13. The DGT Multilingual Translation Memory of the Acquis Communautaire DGT-TM http://langtech.jrc.it/DGT-TM.html

14. Parallel data collected from the Web by University of Uppsala 90 languages, 3800 language 2,7B parallel units Opus http://opus.lingfil.uu.se

15. open European language resource infrastructure http://www.meta-net.eu

16. Data for SMT training

17. PLATFORM

18. Moses toolkit [ttable-file] 0 0 5 /.../unfactored/model/phrase-table.0-0.gz % ls steps/1/LM_toy_tokenize.1* | cat steps/1/LM_toy_tokenize.1 steps/1/LM_toy_tokenize.1.DONE steps/1/LM_toy_tokenize.1.INFO steps/1/LM_toy_tokenize.1.STDERR steps/1/LM_toy_tokenize.1.STDERR.digest steps/1/LM_toy_tokenize.1.STDOUT % train-model.perl \ --corpus factored-corpus/proj-syndicate \ --root-dir unfactored \ --f de --e en \ --lm 0:3:factored-corpus/surface.lm:0 % moses -f moses.ini -lmodel-file "0 0 3../lm/europarl.srilm.gz“ use-berkeley = true alignment-symmetrization-method = berkeley berkeley-train = $moses-script- dir/ems/support/berkeley-train.sh berkeley-process = $moses-script- dir/ems/support/berkeley-process.sh berkeley-jar = /your/path/to/berkeleyaligner- 2.1/berkeleyaligner.jar berkeley-java-options = "-server -mx30000m -ea" berkeley-training-options = "-Main.iters 5 5 - EMWordAligner.numThreads 8" berkeley-process-options = "- EMWordAligner.numThreads 8" berkeley-posterior = 0.5 tokenize in: raw-stem out: tokenized-stem default-name: corpus/tok pass-unless: input-tokenizer output-tokenizer template-if: input-tokenizer IN.$input- extension OUT.$input-extension template-if: output-tokenizer IN.$output- extension OUT.$output-extension parallelizable: yes working-dir = /home/pkoehn/experiment wmt10-data = $working-dir/data

19. build your own MT engine

20. Tilde / Coordinator LATVIA University of Edinburgh UK Uppsala University SWEDEN Copehagen University DENMARK University of Zagreb CROATIA Moravia CZECH REPUBLIC SemLab NETHERLANDS

21. Cloud-based self-service MT factory Repository of parallel and monolingual corpora for MT generation Automated training of SMT systems from specified collections of data Users can specify particular training data collections and build customised MT engines from these collections Users can also use LetsMT! platform for tailoring MT system to their needs from their non- public data

22. Stores SMT training data Supports different formats – TMX, XLIFF, PDF, DOC, plain text Converts to unified format Performs format conversions and alignment Resource Repository

23. Put users in control of their data Fully public or fully private should not be the only choice Data can be used for MT generation without exposing it Empower users to create custom MT engines from their data user-driven machine translation

24. Integration with CAT tools Integration in web pages Integration in web browsers API-level integration integration

25. Integration of MT in SDL Trados

29. use case FORTERA

30. EVALUATION

31. Keyboard-monitoring of post- editing (O´Brien, 2005) Productivity of MS Office localization (Schmidtke, 2008) 5-10% productivity gain for SP, FR, DE Adobe (Flournoy and Duran, 2009) 22%-51% productivity increase for RU, SP, FR Autodesk Moses SMT system (Plitt and Masselot, 2010) 74% average productivity increase for FR, IT, DE, SP Previous Work

32. Evaluation at Tilde Latvian:  About 1,6 M native speakers  Highly inflectional - ~22M possible word forms in total  Official EU language Tilde English – Latvian MT system IT Software Localization Domain Evaluation of translators’ productivity

33. English-Latvian data Bilingual corpusParallel units Localization TM1 290 K DGT-TM1 060 K OPUS EMEA970 K Fiction660 K Dictionary data510 K Web corpus900 K Total5 370 K Monolingual corpusWords Latvian side of parallel corpus 60 M News (web)250 M Fiction9 M Total, Latvian319 M

34. MT Integration into Localization Workflow Evaluate original / assign Translator and Editor Analyze against TMs Translate using translation suggestions for TMs and MT Evaluate translation quality / Edit Fix errors Ready translation MT translate new sentences

35. Key interest of localization industry is to increase productivity of translation process while maintaining required quality level Productivity was measured as the translation output of an average translator in words per hour 5 translators participated in evaluation including both experienced and new translators Evaluation of Productivity

36. Performed by human editors as part of their regular QA process Result of translation process was evaluated, editors did not know was or was not MT applied to assist translator Comparison to reference is not part of this evaluation Tilde standard QA assessment form was used covering the following text quality areas:  Accuracy  Spelling and grammar  Style  Terminology Evaluation of Quality

37. QA Grades Error Score (sum of weighted errors) Resulting Quality Evaluation 0…9Superior 10…29Good 30…49Mediocre 50…69Poor >70Very poor Tilde Localization QA assessment applied in the evaluation

38. Evaluation data ► 54 documents in IT domain ► 950-1050 adjusted words in each document ► Each document was split in half: ► the first part was translated using suggestions from TM only ► the second half was translated using suggestions from both TM and MT

39. % productivity 32.9% * * Skadiņš R., Puriņš M., Skadiņa I., Vasiļjevs A., Evaluation of SMT in localization to under-resourced inflected language, in Proceedings of the 15th International Conference of the European Association for Machine Translation EAMT 2011, p. 35-40, May 30-31, 2011, Leuven, Belgium Latvian

40. Evaluation at Moravia ► IT Localization domain ► Systems trained on the LetsMT platform ► English - Czech translation  25.1% productivity increase  Error score increase from 19 to 27, still at the GOOD grade (<30) ► English – Polish translation  28.5% productivity increase  Error score increase from 16.8 to 23.6, still at the GOOD grade (<30)

41. % productivity 25% *For Czech and Polish formal evaluation was done by Moravia Foror Slovak productivity increase was estimated by Fortera 28.5% Slovak*Polish 25.1% Czech

42. MORE DATA

43. corpora collection tools comparability metrics named entity recognition tools terminology extraction tools ACCURAT TOOLKIT

44. use case AUTOMOTIVE MANUFACTURER

45. very small translation memories (just 3500 sentences) no in-domain corpora in target languages no money for expensive developments ?

46. Terminology extraction Web crawling parallel monolingual Parallel data extraction from comparable corpora data collection workflow

47. TMs Terminology glossary Parallel phrases Parallel Named Entities Monolingual target language corpus Resulting data

48. General domain data as a basis Domain specific language model Impose domain specific terminology, named entity translations Add linguistic knowledge atop of statistical components SMT Training

49. right data & right tools

50. tilde.com technologies for smaller languages The research within the LetsMT! project leading to these results has received funding from the ICT Policy Support Programme (ICT PSP), Theme 5 – Multilingual web, grant agreement no 250456