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CSA4050: Advanced Techniques in NLP

CSA4050: Advanced Techniques in NLP. Machine Translation II Direct MT Transfer MT Interlingual MT. History – Pre ALPAC. 1952 – First MT Conference (MIT) 1954 – Georgetown System (word for word based) successfully translated 49 Russian sentences

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CSA4050: Advanced Techniques in NLP

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  1. CSA4050: Advanced Techniques in NLP Machine Translation II Direct MT Transfer MT Interlingual MT CSA4050 Machine Translation II

  2. History – Pre ALPAC • 1952 – First MT Conference (MIT) • 1954 – Georgetown System (word for word based) successfully translated 49 Russian sentences • 1954 – 1965 – Much investment into brute force empirical approach – crude word-for-word techniques with limited reshuffling of output • ALPAC (Automatic Language Processing Advisory Committee) Report concludes that research funds should be directed into more fundamental linguistic research CSA4050 Machine Translation II

  3. History – Post ALPAC • 1965-1970 • Operational Systems approach: SYSTRAN (eventually became the basis for babelfish) • University centres established in Grenoble (CETA), Montreal and Saarbruecken • Systems developed on the basis of linguistic and non-linguistic representations 1970-1990 • Ariane (Dependency Grammar) • TAUM METEO (Metamorphoses Grammars) • EUROTRA (multilingual intermediate representations) • ROSETTA (Landsbergen) interlingua based • BSO (Witkam) – Esperanto • 1990- Data Driven Translation Systems CSA4050 Machine Translation II

  4. MT Methods MT Direct MT Rule-Based MT Data-Driven MT Transfer Interlingua EBMT SMT CSA4050 Machine Translation II

  5. source text target text Basic Architecture:Direct Translation • Basic idea • language pair specific • no intermediate representation- pipeline architecture CSA4050 Machine Translation II

  6. Staged Direct MT (En/Jp) CSA4050 Machine Translation II

  7. Direct TranslationAdvantages • Exploits fact that certain potential ambiguities can be left unresolvedwall -wand/mauer – parete/muro • Designers can concentrate more on special cases where languages differ. • Minimal resources necessary: a cheap bilingual dictionary & rudimentary knowledge of target language suffices. • Translation memories are a (successful and much used) development of this approach. CSA4050 Machine Translation II

  8. Direct TranslationDisadvantages • Computationally naive • Basic model: word-for-word translation + local reordering (e.g. to handle adj+noun order) • Linguistically naive: • no analysis of internal structure of input, esp. wrt the grammatical relationships between the main parts of sentences. • no generalisation; everything on a case-by-case basis. • Generally, poor translation • except in simple cases where there is lots of isomorphism between sentences. CSA4050 Machine Translation II

  9. Transfer Model of MT • To overcome language differences, first build a more abstract representation of the input. • The translation process as such (called transfer) operates upon at the level of the representation. • This architecture assumes • analysis via some kind of parsing process. • synthesis via some kind of generation. CSA4050 Machine Translation II

  10. Basic Architecture:Transfer Model source representation target representation transfer analysis generation target text source text CSA4050 Machine Translation II

  11. Transfer Rules In General there are two kinds of transfer rule: • Structural Transfer Rules: these deal with differences in the syntactic structures. • Lexical Transfer Rules: these deal with cross lingual mappings at the level of words and fixed phrases. CSA4050 Machine Translation II

  12. Structural Transfer Rule NPs(Adjs,Nouns)  NPt(Nount,Adjt) CSA4050 Machine Translation II

  13. intermediate-representation-1 an old man gardening was • delete initial there • make gardening modify NP existential-there-sentence there was an old man gardening • reverse order of NP/modifier intermediate-representation-2 gardening an old man was • lexical transfer japanese-s niwa no teire o suru ojiisan ita

  14. More Structural Transfer Rules CSA4050 Machine Translation II

  15. Lexical Transfer • Easy cases are based on bilingual dictionary lookup. • Resolution of ambiguitiesmay require further knowledge know  savoirknow  connaître • Not necessarily word for wordschimmel  white horse CSA4050 Machine Translation II

  16. Transfer Model • Degree of generalisation depends upon depth of representation: • Deeper the representation, harder it is to do analysis or generation. • Shallower the representation, the larger the transfer component. • Where does ambiguity get resolved? • Number of bilingual components can get large. CSA4050 Machine Translation II

  17. Interlingual Translation:The Vauquois Triangle interlingua increasing depth analysis generation target text source text CSA4050 Machine Translation II

  18. Interlingual Translation • Transfer model requires different transfer rules for each language pair. • Much work for multilingual system. • Interlingual approach eliminates transfer altogether by creating a language independent canonical form known as an interlingua. • Various logic-based schemes have been used to represent such forms. • Other approaches include attribute/value matrices called feature structures. CSA4050 Machine Translation II

  19. Possible Feature Structure for “There was an old man gardening” event gardening type man agent number sg definiteness indef aspect progressive tense past CSA4050 Machine Translation II

  20. Ontological Issues • The designer of an interlingua has a very difficult task. • What is the appropriate inventory of attributes and values? • Clearly, the choice has radical effects on the ability of the system to translate faithfully. • For instance, to handle the muro/parete distinction, the internal/external characteristic of the wall would have to be encoded. CSA4050 Machine Translation II

  21. Feature Structure for “muro” word muro syntax POS class noun type count field buildings semantics type structural position external CSA4050 Machine Translation II

  22. Interlingual Approach Pros and Cons • Pros • Portable (avoids N2 problem) • Because representation is normalised structural transformations are simpler to state. • Explanatory Adequacy • Cons • Difficult to deal with terms on primitive level: • universals? • Must decompose and reassemble concepts • Useful information lost (paraphrase) • In practice, works best in small domains. CSA4050 Machine Translation II

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