FROM TRANSLATION MACHINE THEORY TO MACHINE TRANSLATION THEORY

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) • sentence alignment (Gale & Church 1991) • word alignment (Och & Ney 2003) …and a plethora of papers on how to improve these

RECENT RESURGENCE OF LINGUISTICS • 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) • …

TRANSLATION-THEORETIC MODELLING OF MT

MT AND FUNCTIONAL TRANSLATION THEORY (1) • Skopos theory (Reiss & Vermeer 1984) • pragmalinguistic model (House 1997), function and loyalty (Nord 1997, 2006) functional equivalence change in function documentary instrumental over covert

MT AND FUNCTIONAL TRANSLATION THEORY (2) • aimed at functional equivalence (but does a machine or a GT user know? ) • aimed at instrumental (but in fact rather documentary; ethical dimensions? )

MT AND FUNCTIONAL TRANSLATION THEORY (3) • MT and its lack of translation–functional considerations in system design (Schmidt in print) • “human, purposeful action”-theoretic conception of translation as hindrance to acceptance of MT (Rozmyslowicz in print)

KNOWLEDGE TRANSFER TS -> MT

CROCO

CROCO STRUCTURE: MULTILINGUAL + Metainformation + Po. S tagging + Morphology + Sense relations Translation Corpus Word layer Chunk layer Register-controlled Corpus Word layer Alignment layers Chunk layer Clause layer Sentence layer + Phrase structure + Grammatical functions

FUNCTION SHIFTS (TYPOLOGICAL DIFFERENCES) SUBJ FIN Tray 1 holds In Fach 1 PROBJ DOBJ können FIN up to 125 sheets bis zu 125 Blatt Papier SUBJ eingelegt werden PRED 12

FUNCTION SHIFTS PER REGISTER AND TRANSLATION DIRECTION 50 45 40 35 30 25 20 15 10 5 0 subj-*adv subj-*obj *adv-subj R G 2 E_ IN ST R E 2 G_ IN ST N IO FI E_ G 2 CT FI G_ E 2 CT IO N SA Y ES E_ G 2 E 2 G_ ES SA Y *obj-subj

GRAMMATICAL FUNCTIONS IN THEME POSITION

MT AND TRANSLATION FACTORS: REGISTER AND TRANSLATION DIRECTION • often spoken of domains, but that term is too vague • Kurokawa et al. (2009) – training translation models according to translation direction (A), and without (B) – for a performance of (A) equivalent to (B), they needed only ca. 1/5 of the data size • feature selection problem: which feature per register and translation direction (e. g. Diwersy et al. 2013, also an overview in Oakes & Ji 2012)

POST-EDITING

INCREASING ROLE OF 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

CRITT TPR DATABASE Ø project coordinator: Copenhagen Business School Ø English-German data collection at FTSK in Germersheim Ø translation vs. post-editing vs. (blind) editing Ø 6 source texts (ST) with different complexity levels (Hvelplund 2011) Ø 12 professional translators, 12 semi-professional translators Ø MT system: Google Translate Ø eye-tracking (Tobii TX 300), key-logging (Translog II), retrospective questionnaires

EYE-TRACKING AND KEY-LOGGING POSTEDITING

PROCESSING TIMES cf. Carl, Gutermuth & Hansen-Schirra in print

Word number PROCESSING STYLES Time

Word number PROCESSING PATTERNS Time

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: Als Zeichen des Widerstands gegen die Chinesische Regierung. . . ‘As sign the-GEN. resistance against the Chinese government…’

LACK OF CONSISTENCY 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.

OVERVIEW

CONCLUSIONS AND SUGGESTIONS

FUTURE WORK • Entrenchment of MT in TS (theory): – common ground – more acceptance – improved description of MT workflow for the translator – imrpoved task descriptions for PE

SOME TENTATIVE SUGGESTIONS TO OURSELVES FOR BETTER TASK DESCRIPTION BASED ON TRANSLATOR CONCEPTS Task description Function of the text (e. g. Nord 2006, House 1997) terminological idiomaticity As little as possible documentary (rapid PE) Conceptually equivalent, nonterms but also dispreferred or deprecated terms may be used Unidiomatic, but understandable wording may remain (disambiguated at word level!) As much as possible (full PE) Only allowed terms Phraseology can be used according to the domain Covert instrumental Intermediate levels Overt instrumental (usable, but identifiable as translation) Only terms, but also dispreferred and maybe deprecated Idiomatic, but also non-standard phraseology

THANK YOU FOR YOUR ATTENTION!. . . AND YOUR QUESTIONS, COMMENTS, . . .

REFERENCES (1) Bertoldi, Nicola, and Marcello Federico. 2009. “Domain Adaption for Statistical Machine Translation with Monolingual Resources. ” In Proceedings of the Fourth Workshop on Statistical Machine Translation, 182– 189. Athens, Greece: Association for Computational Linguistics. Brown, Peter E. , Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. “The Mathematics of Statistical Machine Translation: Parameter Estimation. ” Computational Linguistics 2 (19): 263– 311. Eisele, Andreas. 2006. “Parallel Corpora and Phrase-based Statistical Machine Translation for New Language Pairs via Multiple Intermediaries. ” In 5 th 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, and Yu Chen. 2008. “Hybrid Architectures for Multi-Engine Machine Translation. ” In Translating and the Computer 30. London, UK. Fox, Heidi J. 2002. “Phrasal Cohesion and Statistical Machine Translation. ” In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 304– 11. Philadelphia: ACL. Gale, William A, and Kenneth W Church. 1993. “A Program for Aligning Sentences in Bilingual Corpora. ” Computational Linguistics 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 Proceedings of 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.

REFERENCES (2) Kurokawa, David, Cyril Goutte, and Pierre Isabelle. 2009. “Automatic Detection of Translated Text and Its Impact on Machine Translation. ” Proceedings. MT Summit XII, The Twelfth Machine Translation Summit International Association for Machine Translation Hosted by the Association for Machine Translation in the Americas. Lapshinova-Koltunski, Ekaterina. 2013. “VARTRA: A Comparable Corpus for the Analysis of Translation Variation. ” In Proceedings of the 6 th Workshop on Building and Using Comparable Corpora, 77– 86. Sofia, Bulgaria. Lembersky, Gennadi, Noam Ordan, and Shuly Wintner. 2012. “Language Models for Machine Translation: Original Vs. Translated Texts. ” Computational Linguistics 38 (4): 799– 825. Nord, Christiane. 1997. Translating as a Purposeful Activity. Functionalist Approaches Explained. Translation Theories Explained 1. Manchester: Jerome. ———. 2006. “Translating for Communicative Purposes Across Culture Boundaries. ” Journal of Translation Studies 9 (1): 43– 60. Och, Franz-Josef, and Hermann Ney. 2003. “A Systematic Comparison of Various Statistical Alignment Models. ” Computational Linguistics 29 (1): 19– 51. Reiss, Katharina, and Hans J. Vermeer. 1984. Grundlegung Einer Allgemeinen Translationstheorie. Linguistische Arbeiten 147. Tübingen: M. Niemeyer.