CSA4050: Advanced Techniques in NLP
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Transcript of CSA4050: Advanced Techniques in NLP
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Jan 2005 CSA4050 Machine Translation II 1
CSA4050: Advanced Techniques in NLP
Machine Translation II• Direct MT• Transfer MT• Interlingual MT
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Jan 2005 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
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Jan 2005 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
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Jan 2005 CSA4050 Machine Translation II 4
MT Methods
MT
Direct MT Rule-Based MT Data-Driven MT
Transfer Interlingua EBMT SMT
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Jan 2005 CSA4050 Machine Translation II 5
Basic Architecture:Direct Translation
source text target text
Basic idea - language pair specific- no intermediate representation- pipeline architecture
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Jan 2005 CSA4050 Machine Translation II 6
Staged Direct MT (En/Jp)
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Jan 2005 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.
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Jan 2005 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.
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Jan 2005 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.
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Basic Architecture:Transfer Model
source text target text
sourcerepresentation
targetrepresentation
analysis generation
transfer
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Jan 2005 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.
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Structural Transfer Rule
NPs(Adjs,Nouns) NPt(Nount,Adjt)
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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
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Jan 2005 CSA4050 Machine Translation II 14
More Structural Transfer Rules
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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
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Jan 2005 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.
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Jan 2005 CSA4050 Machine Translation II 17
Interlingual Translation:The Vauquois Triangle
source text target text
interlingua
analysis generation
increasing depth
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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.
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Jan 2005 CSA4050 Machine Translation II 19
Possible Feature Structure for “There was an old man gardening”
event gardening
type managent number sg
definiteness indef
aspect progressivetense past
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Jan 2005 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.
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Jan 2005 CSA4050 Machine Translation II 21
Feature Structure for “muro”
word muro
syntax POS class nountype count
field buildingssemantics type structural
position external
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Jan 2005 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.