CSA4050: Advanced Techniques in NLP

Jan 2005 CSA4050 Machine Translation II 1

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

• 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


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


MT Methods

MT

Direct MT Rule-Based MT Data-Driven MT

Transfer Interlingua EBMT SMT


Basic Architecture:Direct Translation

source text target text

Basic idea - language pair specific- no intermediate representation- pipeline architecture


Staged Direct MT (En/Jp)


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.


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.


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.


Basic Architecture:Transfer Model


sourcerepresentation

targetrepresentation

analysis generation

transfer


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.


Structural Transfer Rule

NPs(Adjs,Nouns) NPt(Nount,Adjt)

13

existential-there-sentence

there was an old man gardening

intermediate-representation-1

an old man gardening was

intermediate-representation-2

gardening an old man wasjapanese-s

niwa no teire o suru ojiisan ita

• delete initial there

• make gardening modify NP

• reverse order of NP/modifier

• lexical transfer


More Structural Transfer Rules


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


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.


Interlingual Translation:The Vauquois Triangle


interlingua

analysis generation

increasing depth


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.


Possible Feature Structure for “There was an old man gardening”

event gardening

type managent number sg

definiteness indef

aspect progressivetense past


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.


Feature Structure for “muro”

word muro

syntax POS class nountype count

field buildingssemantics type structural

position external


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.

Documents

CSA4050: Advanced Techniques in NLP