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From Translation Machine Theory to Machine Translation Theory – some initial Thoughts

From Translation Machine Theory to Machine Translation Theory – some initial Thoughts. Oliver Čulo Universität Mainz culo@uni-mainz.de. MT as Translation Machine Theory. Topics of ( early ) SMT. Calculating translation models (Brown et al. 1993)

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From Translation Machine Theory to Machine Translation Theory – some initial Thoughts

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  1. From Translation MachineTheorytoMachine Translation Theory– some initial Thoughts Oliver Čulo Universität Mainz culo@uni-mainz.de

  2. MT as Translation MachineTheory

  3. Topics of (early) SMT • Calculating translation models (Brown et al. 1993) • sentence alignment (Gale & Church 1991) • word alignment (Och & Ney 2003) …and a plethora of papers on how to improve these

  4. Recentresurgenceoflinguistics • MT and the phrase (Fox 2002, Koehn et al. 2003, Eisele 2006) • MT and dependency (Ding & Palmer 2005, Quirk et al. 2005, Žabokrtský et al. 2008) • hybrid architectures (Eisele et al. 2008) • domain adaption (Koehn & Schroeder 2007, Bertoldi & Federico 2009) • factored models (Koehn & Hoang 2007) • …

  5. Translation-theoreticmodellingof MT

  6. MT andFunctional Translation Theory (1) • Skopostheory (Reiss & Vermeer 1984) • pragmalinguisticmodel (House 1997), function and loyalty (Nord 1997, 2006) functional equivalence  change in function documentary  instrumental over covert

  7. MT andFunctional Translation Theory (2) • aimedatfunctionalequivalence (but does a machineor a GT userknow?) • aimedat instrumental (but in factratherdocumentary; ethicaldimensions?)

  8. MT andFunctional Translation Theory(3) • MT andits lack oftranslation–functionalconsiderations in system design (Schmidt in print) • “human, purposeful action”-theoretic conception of translation as hindrance to acceptance of MT (Rozmyslowicz in print)

  9. Knowledgetransfer TS -> MT

  10. CroCo

  11. + Metainformation + PoS tagging + Morphology + Sense relations + Phrase structure + Grammatical functions CroCo structure: multilingual Translation Corpus Register-controlled Corpus Word layer Word layer Alignmentlayers Chunk layer Chunk layer Clause layer Clause layer Sentence layer Sentence layer

  12. FunctionShifts(TypologicalDifferences) SUBJ FIN DOBJ Tray 1 holds upto 125 sheets In Fach 1 können bis zu 125 Blatt Papier eingelegt werden PROBJ FIN SUBJ PRED 12

  13. FunctionShifts per Register and Translation Direction

  14. Grammaticalfunctions in themeposition

  15. MT and Translation Factors:Register and Translation Direction • oftenspokenofdomains, but thattermistoovague • Kurokawa et al. (2009) • trainingtranslationmodelsaccordingtotranslationdirection (A), andwithout (B) • for a performanceof (A) equivalentto (B), theyneededonly ca. 1/5 ofthedatasize • featureselectionproblem: whichfeature per registerandtranslationdirection (e.g. Diwersy et al. 2013, also an overview in Oakes & Ji 2012)

  16. Post-Editing

  17. Increasingroleof MT in translation • MT integrated into Translation Memories, many translation workflows (SDL 2011, Bajon et al. 2012, O‘Brien 2012) • as MT needs to be post-edited, in consequence post-editing becomes a more and more important component of the translator’s job profile

  18. CRITT TPR Database • projectcoordinator: Copenhagen Business School • English-German datacollectionat FTSK in Germersheim • translation vs. post-editing vs. (blind) editing • 6 sourcetexts (ST) with different complexitylevels (Hvelplund 2011) • 12 professional translators, 12 semi-professional translators • MT system: Google Translate • eye-tracking (Tobii TX 300), key-logging (Translog II), retrospectivequestionnaires

  19. Eye-Tracking and Key-Logging Post-editing

  20. Processing Times cf. Carl, Gutermuth & Hansen-Schirra in print

  21. Processing Styles Word number Word number Time Time

  22. Processing Patterns Word number Word number Time Time

  23. Interference ST: In a gesture sure to rattle the Chinese Government, Steven Spielberg pulled out of the Beijing Olympics to protest against China's backing for Sudan's policy in Darfur. HT: AlsZeichen des Widerstandsgegen die ChinesischeRegierung... ‘As sign the-GEN. resistance against the Chinese government…’

  24. Lack ofConsistency ST: Killer nurse receives four life sentences. Hospital nurse C.N. was imprisoned for life today for the killing of four of his patients. PE: Killer-Krankenschwester zu viermal lebenslanger Haft verurteilt. Der Krankenpfleger C.N. wurde heute auf Lebenszeit eingesperrt für die Tötung von vier seiner Patienten. ‘Killer nurse.FEM to four times lifetime imprisonment sentenced. The nurse.MASC C.N. was today on lifetime imprisoned for the killing of four his.GEN patients.

  25. Overview

  26. ConclusionsandSuggestions

  27. Future work • Entrenchmentof MT in TS (theory): • commonground • moreacceptance • improveddescriptionof MT workflowforthetranslator • imrpovedtaskdescriptionsfor PE

  28. Some tentative suggestions to ourselves for better task description based on translator concepts

  29. ThankYouforYour Attention! ... andYourQuestions, Comments, ...

  30. References (1) Bertoldi, Nicola, and Marcello Federico. 2009. “Domain Adaption for Statistical Machine Translation with Monolingual Resources.” In ProceedingsoftheFourth Workshop on Statistical Machine Translation, 182–189. Athens, Greece: AssociationforComputationalLinguistics. Brown, Peter E., Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. “The Mathematicsof Statistical Machine Translation: Parameter Estimation.” ComputationalLinguistics 2 (19): 263–311. Eisele, Andreas. 2006. “Parallel Corporaand Phrase-based Statistical Machine Translation for New Language Pairs via Multiple Intermediaries.” In 5th International Conference on Language Resources and Evaluation (LREC) 2006. Eisele, Andreas, Christian Federmann, Hans Uszkoreit, Saint-Amand Hervé, Martin Kay, Michael Jellinghaus, Sabine Hunsicker, Teresa Herrmann, andYu Chen. 2008. “Hybrid Architecturesfor Multi-Engine Machine Translation.” In Translatingandthe Computer 30. London, UK. Fox, Heidi J. 2002. “PhrasalCohesionand Statistical Machine Translation.” In Proceedingsofthe Conference on EmpiricalMethods in Natural Language Processing (EMNLP), 304–11. Philadelphia: ACL. Gale, William A, and Kenneth W Church. 1993. “A ProgramforAligningSentences in Bilingual Corpora.” ComputationalLinguistics 19 (1): 75–102. House, Juliane. 1997. Translation Quality Assessment. A Model Revisited. Tübingen: Gunter Narr Verlag. Koehn, Philipp, Franz Josef Och, and Daniel Marcu. 2003. “Statistical Phrase-Based Translation.” In Proceedingsof HLT-NAACL 2003, 127–133. Koehn, Philipp, and Josh Schroeder. 2007. “Experiments in Domain Adaptation for Statistical Machine Translation.” In ACL Workshop on Machine Translation 2007.

  31. References (2) Kurokawa, David, Cyril Goutte, and Pierre Isabelle. 2009. “AutomaticDetectionofTranslated Text andIts Impact on Machine Translation.” Proceedings. MT Summit XII, The TwelfthMachine Translation Summit International AssociationforMachine Translation HostedbytheAssociationforMachine Translation in theAmericas. Lapshinova-Koltunski, Ekaterina. 2013. “VARTRA: A Comparable Corpus forthe Analysis of Translation Variation.” In Proceedingsofthe 6th Workshop on BuildingandUsingComparableCorpora, 77–86. Sofia, Bulgaria. Lembersky, Gennadi, Noam Ordan, andShulyWintner. 2012. “Language Models forMachine Translation: Original Vs. Translated Texts.” ComputationalLinguistics 38 (4): 799–825. Nord, Christiane. 1997. Translatingas a PurposefulActivity. FunctionalistApproachesExplained. Translation TheoriesExplained 1. Manchester: Jerome. ———. 2006. “TranslatingforCommunicativePurposesAcross Culture Boundaries.” Journal of Translation Studies 9 (1): 43–60. Och, Franz-Josef, and Hermann Ney. 2003. “A SystematicComparisonofVarious Statistical Alignment Models.” ComputationalLinguistics 29 (1): 19–51. Reiss, Katharina, and Hans J. Vermeer. 1984. Grundlegung Einer Allgemeinen Translationstheorie. Linguistische Arbeiten 147. Tübingen: M. Niemeyer.

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