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Language Divergences and Solutions

Language Divergences and Solutions. Advanced Machine Translation Seminar Alison Alvarez. Overview. Introduction Morphology Primer Translation Mismatches Types Solutions Translation Divergences Types Solutions Different MT Systems Generation Heavy Machine Translation DUSTer.

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Language Divergences and Solutions

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  1. Language Divergences and Solutions Advanced Machine Translation Seminar Alison Alvarez

  2. Overview • Introduction • Morphology Primer • Translation Mismatches • Types • Solutions • Translation Divergences • Types • Solutions • Different MT Systems • Generation Heavy Machine Translation • DUSTer

  3. Source ≠ Target • Languages don’t encode the same information in the same way • Makes MT complicated • Keeps all of us employed

  4. Morphology in a Nutshell • Morphemes are word parts • Work +er • Iki +ta +ku +na +ku +na +ri +ma +shi +ta • Types of Morphemes • Derivational: makes new word • Inflectional: adds information to an existing word

  5. Morphology in a Nutshell • Analytic/Isolating • little or no inflectional morphology, separate words • Vietnamese, Chinese • I was made to go • Synthetic • Lots of inflectional morphology • Fusional vs. Agglutinating • Romance Languages, Finnish, Japanese, Mapudungun • Ika (to go) +se (to make/let) +rare (passive) +ta (past tense) • He need +s (3rd person singular) it.

  6. Translation Differences • Types • Translation Mismatches • Different information from source to target • Translation Divergences • Same information from source to target, but the meaning is distributed differently in each language

  7. Translation Mismatches • “…the information that is conveyed is different in the source and target languages” • Types: • Lexical level • Typological level

  8. Lexical Mismatches • A lexical item in one language may have more distinctions than in another Brother 弟 otouto Younger Brother 兄さん Ani-san Older Brother

  9. Typological Mismatches • Mismatch between languages with different levels of grammaticalization • One language may be more structurally complex • Source marking, Obligatory Subject

  10. Typological Mismatches • Source: Quechua vs. English • (they say) s/he was singing --> takisharansi • taki (sing) +sha (progressive) +ra (past) + n (3rd sg) +si (reportative) • Obligatory Arguments: English vs. Japanese • Kusuri wo Nonda --> (I, you, etc.) took medicine. • Makasemasu! -->(I’ll) leave (it) to (you)

  11. Translation Mismatch Solutions • More information --> Less information (easy) • Less information --> More information (hard) • Context clues • Language Models • Generalization • Formal representations

  12. Translation Divergences • “…the same information is conveyed in source and target texts” • Divergences are quite common • Occurs in about 1 out of every three sentences in the TREC El Norte Newspaper corpus (Spanish-English) • Sentences can have multiple kinds of divergences

  13. Translation Divergence Types • Categorial Divergence • Conflational Divergence • Structural Divergence • Head Swapping Divergence • Thematic Divergence

  14. Categorial Divergence • Translation that uses different parts of speech • Tener hambre (have hunger) --> be hungry • Noun --> adjective

  15. Conflational Divergence • The translation of two words using a single word that combines their meaning • Can also be called a lexical gap • X stab Z --> X darpuñaladas a Z (X give stabs to Z) • glastuinbouw --> cultivation under glass

  16. Structural Divergence • A difference in the realization of incorporated arguments • PP to Object • X entrar en Y (X enter in Y) --> X enter Y • X ask for a referendum --> X pedir un referendum (ask-for a referendum)

  17. Head Swapping Divergence • Involves the demotion of a head verb and the promotion of a modifier verb to head position S NP VP N V PP I ran into the room. S NP VP N V PP VP Yo entro en el cuarto corriendo

  18. Thematic Divergence • This divergence occurs when sentence arguments switch argument roles from one language to another • X gustar a Y (X please to Y) --> Y like X

  19. Divergence Solutions and Statistical/EBMT Systems • Not really addressed explicitly in SMT • Covered in EBMT only if it is covered extensively in the data

  20. Divergence Solutions and Transfer Systems • Hand-written transfer rules • Automatic extraction of transfer rules from bi-texts • Problematic with multiple divergences

  21. Divergence Solutions and Interlingua Systems • Mel’čuk’s Deep Syntactic Structure • Jackendoff’s Lexical Semantic Structure • Both require “explicit symmetric knowledge” from both source and target language • Expensive

  22. Divergence Solutions and Interlingua Systems John swam across a river [event CAUSE JOHN [event GO JOHN [path ACROSS JOHN [position AT JOHN RIVER]]] [manner SWIM+INGLY]] Juan cruza el río nadando

  23. Generation-Heavy MT • Built to address language divergences • Designed for source-poor/target-rich translation • Non-Interlingual • Non-Transfer • Uses symbolic overgeneration to account for different translation divergences

  24. Generation-Heavy MT • Source language • syntactic parser • translation lexicon • Target language • lexical semantics, categorial variations & subcategorization frames for overgeneration • Statistical language model

  25. GHMT System

  26. Analysis Stage • Independent of Target Language • Creates a deep syntactic dependency • Only argument structure, top-level conceptual nodes & thematic-role information • Should normalize over syntactic & morphological phenomena

  27. Translation Stage • Converts SL lexemes to TL lexemes • Maintains dependency structure

  28. Analysis/Translation Stage GIVE (v) [cause go] I agent STAB (n) theme JOHN goal

  29. Generation Stage • Lexical & Structural Selection • Conversion to a thematic dependency • Uses syntactic-thematic linking map • “loose” linking • Structural expansion • Addresses conflation & head-swapped divergences • Turn thematic dependency to TL syntactic dependency • Addresses categorial divergence

  30. Generation Stage: Structural Expansion

  31. Generation Stage • Linearization Step • Creates a word lattice to encode different possible realizations • Implemented using oxyGen engine • Sentences ranked & extracted • Nitrogen’s statistical extractor

  32. Generation Stage

  33. GHMT Results • 4 of 5 Spanish-English divergences “can be generated using structural expansion & categorial variations” • The remaining 1 out of 5 needed more world knowledge or idiom handling • SL syntactic parser can still be hard to come by

  34. Divergences and DUSTer • Helps to overcome divergences for word alignment & improve coder agreement • Changes an English sentence structure to resemble another language • More accurate alignment and projection of dependency trees without training on dependency tree data

  35. DUSTer • Motivation for the development of automatic correction of divergences • “Every Language Pair has translation divergences that are easy to recognize” • “Knowing what they are and how to accommodate them provides the basis for refined word level alignment” • “Refined word-level” alignment results in improved projection of structural information from English to another language

  36. DUSTer

  37. DUSTer • Bi-text parsed on English side only • “Linguistically Motivated” & common search terms • Conducted on Spanish & Arabic (and later Chinese & Hindi) • Uses all of the divergences mentioned before, plus a “light verb” divergence • Try  put to trying  poner a prueba

  38. DUSTer Rule Development Methods • Identify canonical transformations for each divergence type • Categorize English sentences into divergence type or “none” • Apply appropriate transformations • Humans align E  E’  foreign language

  39. DUSTer Rules # "kill" => "LightVB kill(N)" (LightVB = light verb) # Presumably, this will work for "kill" => "give death to” # "borrow" => "take lent (thing) to” # "hurt" => "make harm to” # "fear" => "have fear of” # "desire" => "have interest in” # "rest" => "have repose on” # "envy" => "have envy of” type1.B.X [English{2 1 3} Spanish{2 1 3 4 5} ] [ Verb<1,i,CatVar:V_N> [ Noun<2,j,Subj> ] [ Noun<3,k,Obj> ] ] <--> [ LightVB<1,Verb>[ Noun<2,j,Subj> ] [ Noun<3,i,Obj> ] [ Oblique<4,Pred,Prep> [ Noun<5,k,PObj> ] ] ]

  40. DUSTer Results

  41. Conclusion • Divergences are common • They are not handled well by most MT systems • GHMT can account for divergences, but still needs development • DUSTer can handle divergences through structure transformations, but requires a great deal of linguistic knowledge

  42. The End • Questions?

  43. References Dorr, Bonnie J., "Machine Translation Divergences: A Formal Description and Proposed Solution," Computational Linguistics, 20:4, pp. 597--633, 1994. Dorr, Bonnie J. and Nizar Habash, "Interlingua Approximation: A Generation-Heavy Approach", In Proceedings of Workshop on Interlingua Reliability, Fifth Conference of the Association for Machine Translation in the Americas, AMTA-2002,Tiburon, CA, pp. 1--6, 2002 Dorr, Bonnie J., Clare R. Voss, Eric Peterson, and Michael Kiker, "Concept Based Lexical Selection," Proceedings of the AAAI-94 fall symposium on Knowledge Representation for Natural Language Processing in Implemented Systems, New Orleans, LA, pp. 21--30, 1994. Dorr, Bonnie J., Lisa Pearl, Rebecca Hwa, and Nizar Habash, "DUSTer: A Method for Unraveling Cross-Language Divergences for Statistical Word-Level Alignment," Proceedings of the Fifth Conference of the Association for Machine Translation in the Americas, AMTA-2002,Tiburon, CA, pp. 31--43, 2002. Habash, Nizar and Bonnie J. Dorr, "Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translation", In Proceedings of the Fifth Conference of the Association for Machine Translation in the Americas, AMTA-2002,Tiburon, CA, pp. 84--93, 2002. Haspelmath, Martin. Understanding Morphology. Oxford Univeristy Press, 2002. Kameyama, Megumi and Ryo Ochitani, Stanley Peters “Resolving Translation Mismatches With Information Flow” Annual Meeting of the Assocation of Computational Linguistics, 1991

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