Postgraduate Diploma in Translation

1. Postgraduate Diploma in Translation Machine Translation II Direct MT Transfer MT Interlingual MT Machine Translation II

2. Today’s Lecture Part I • Historical Background • Different Translation Models • Direct MT • Transfer Based MT • Interlingual MT Part II • Example Based MT • Statistical MT Machine Translation II

3. 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 Machine Translation II

4. 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 Machine Translation II

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

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

7. Direct TranslationAdvantages • Exploits fact that certain potential ambiguities can be left unresolvedwand/mauer – parete/muro → wall • 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. 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. Machine Translation II

9. Example of Direct Translation • French: Les soldats sont dans le café Machine Translation II

10. Example of Direct Translation • French: Les soldats sont dans le café • English: The soldiers are in the coffee Machine Translation II

11. 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 process Machine Translation II

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

13. The Analysis Problem • The aim of analysis is to transform unstructured text to a structured representation that is easier to translate. • There are two major problems • Ambiguity • Ill formed input: the fact that written language abounds in errors of spelling, repeated words, grammatical errors etc. Machine Translation II

14. Does Ambiguity Matter? • In some cases ambiguity can be ignored or preserved: e.g, they (En) → sie (De) irrespective of gender. • Different language pairs behave in different ways they (En) → ils (Fr) they (En) → elles (Fr) Machine Translation II

15. Does Ambiguity Matter ?(2) • Pauline writes to her friends in Paris. • Ambiguity can remain in French translationPauline écrit à ses amis à Paris • Pauline misses her friends in Paris • Ambiguity has to be resolved À Paris les amis manquent à Pauline Les amis à Paris manquent à Pauline Machine Translation II

16. Two Key Points • Ambiguities combine together. The resulting collection of ambiguities can be very large. • Different varieties of information required to resolve ambiguities. • Grammatical Information (here agreement)John hit Bill then he hit himJohn hit Mary then she hit him • World knowledgePregnant women and men came to the meeting. Machine Translation II

17. Ambiguities Multiply • In the worst case, if we have N ambiguous words, we have 2N ambiguities. • Exponential growth; combinatorial explosion. • Sometimes some of the ambiguities can be ruled out a priori Sam loves presents • but not always: typically there are tens or even hundreds of possible analyses for very ordinary sentences. Machine Translation II

18. Ill-formed Input • Two methods for dealing with it: • Permuting, inserting, deleting words until an analysis is found. • There are so many possibilities that this can easily lead to combinatoral explosion • Relaxing constraints on, e.g., agreement.The problems are interesting, but the solution leave something to be desired • If used generally, can create further ambiguities. • Inverse relationship between robustness and potential for ambiguity. Machine Translation II

19. Transfer • The task of transfer is to take the source interface structure produced by analysis and produce a target interface structure which can be input to the synthesis component. • In general, this transformation is effected by transfer rules. Machine Translation II

20. An Example • I miss London [sentence/pres miss [nounphrase/sing/1 pronoun] [nounphrase London]] • Londres me manque [sentence/pres manquer [nounphrase Londres] [nounphrase/sing/1 pronoun]] source interface structure target interface structure Machine Translation II

21. Transfer Rules • London translates as Londres • miss translates as manquer • First person singular nounphrase translate as first person singular nounphrase • direct object of miss translates as subject of manquer. • subject of miss translates as indirect object of manquer. Machine Translation II

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

23. Lexical TransferWord → Word (usually) • Easy cases are based on bilingual dictionary lookup. • Resolution of ambiguitiesmay require further knowledge know  savoirknow  connaître • Not necessarily word for wordschimmel  white horse Machine Translation II

24. Structural Transfer RuleTree → Tree NPs(Adjs,Nouns)  NPt(Nount,Adjt) Machine Translation II

25. Structural Transfer (1)Passive Constructions. • apples are sold here. (passive) • man verkoopt hier appels (impersonal)one sells here apple • se venden manzanas aqui (reflexive)self they sell apples here Machine Translation II

26. Structural Transfer (2)Adjective/Noun Correspondences • An adjective in En translates as a noun in De • I am hungry • Ich habe hungerI have hunger • Knock on effect: we cannot get the normal translation of very (= sehr in German) • I am very hungry • Ich habe einen riesigen hungerI have a huge hunger Machine Translation II

27. Sometimes knock on effect requires insertion of new information • das für Sam neue Autothe for Sam new car • the car which is new to Sam • The problem here is that • we have to supply a verb (in this case is) • the verb has to have a tense • Neither of these pieces of information were present in the source. Machine Translation II

28. Generally, translation may involve inserting information missing in source. • In English, one cannot avoid the issue of whether a noun phrase is singular or plural • In Japanese, this information can remain unspecified. • Therefore, there is a problem going from Japanese into English • Similarly, one cannot avoid the issue of social relationship between the reader and writer in Japanese, but one can in English. • So there is a (different) problem in going from English to Japanese. Machine Translation II

29. Towards an Interlingua • The transfer problem arises because of differences between source and target interface structures. • The more similar they are, the smaller the transfer problem should be. • There is clearly a relationship between the “depth” of interface structure and the size of the respective components of an MT system. Machine Translation II

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

31. 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 and feature-based schemes have been used to represent such forms. • Other approaches include attribute/value matrices called feature structures. Machine Translation II

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

33. Interlingual Translation.Problem 1: Unnecessarily complex analysis • Basic idea is that source and target interface structures are identical. • With this approach analysis is more complex. • In particular, all distinctions relevant for translation into any target language must be present in the interlingua. • English/Japanese: Analysis of the word sister will have to distinguish between younger and older sister. • Wasteful for an English/French system. Machine Translation II

34. Interlingual Translation.Problem 2: Ensuring identity of S & T • Sam eats only fish • Natural En interlingual representationif e is an eating event with eater Sam, the thing eaten is fish • Natural Jp interlingual representationthere are no eating events with Sam as eater that do not involve fish as object • These representations are intuitively equivalent but they are not identical. • To get from one to the other requires something like a logic for the interlingua which provides a well-defined notion of equivalence. Machine Translation II

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

36. Intrerlingual Systems: Problems 3 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. source interface structure Machine Translation II

37. Summary • More abstract representations are a good thing because they make the job of the transfer component smaller. • Yet there are irreducible differences in the way that languages express the same content. • So the transfer problem cannot be entirely eliminated. Machine Translation II

38. The Generation (Synthesis) Problem • There are typically many ways in which the same content can be expressed. • Sometimes only one of the ways of expressing the content is correct, e.g. • What time is it? • How late is it? • What is the hour? • Difficult to keep a list of contents that should be realised idiomatically. Machine Translation II

39. Synthesis: Problem 2 • There may be no obvious way to realise the content. • Sam saw a cat. It was black. • Sam saw something black. It was a cat. • Sam saw a cat which was black. • There was a black cat. Sam saw it. • How does one select between the alternatives? • Heuristic: stick to the form of the source sentence. Machine Translation II

40. Components of a typical MT system • Source & target lexicons (10,000+ entries) • Morphological rules (50+ rules) • Source analysis rules (50 -100 rules) • Target generation rules (50 - 100 rules) • Transfer component if system is not interlingual (100-1000 rules) • Ten man/years to produce a basic system Machine Translation II