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Globalisation and machine translation

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Assumes one-to-one relation between source symbol and target symbol. one ... situation: online translation, e.g. Babel Fish, descendant of SYSTRAN whose goal ... – PowerPoint PPT presentation

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Title: Globalisation and machine translation


1
Globalisation and machine translation
  • Machine Translation (MT)
  • The decoding paradigm
  • Ambiguity
  • Translation models
  • Interlingua and First Order Predicate Calculus
  • Human involvement
  • Historical note

2
Machine translation
  • The decoding paradigm
  • Assumes one-to-one relation between source symbol
    and target symbol
  • one-to-many (homonymy)
  • one-to-many (hypernym ? hyponyms)
  • many-to-one (hyponyms ? hypernym)
  • hill, mountain ? Berg (German)
  • learn, teach ? leren (Dutch)

3
Machine translation
  • The decoding paradigm
  • Assumes one-to-one relation between source symbol
    and target symbol
  • one-to-many (homonymy)
  • bank ? Ufer, Bank (German)
  • one-to-many (hypernym ? hyponyms)
  • brother ? otooto, oniisan (Japanese)
  • blue ? ?????, ??????? (Russian)
  • many-to-one (hyponyms ? hypernym)
  • hill, mountain ? Berg (German)
  • learn, teach ? leren (Dutch)

4
Machine translation and globalisation
  • Ambiguity
  • I made her duck
  • The possibility of interpreting an expression in
    two or more distinct ways
  • Collins English Dictionary

5
Machine translation
  • Ambiguity
  • Challenge of the translation depends on the level
    of ambiguity that arises
  • This depends on the closeness of the source and
    target languages w.r.t. the following
  • vocabulary
  • homonyms
  • grammar
  • structural ambiguity
  • conceptual structure
  • specificity ambiguity
  • lexical gaps

6
Machine translation
  • Pragmatic approach
  • aim for a rough translation, gist translation
  • Used for multi-lingual information retrieval
  • involve human translators in the process
  • computer-aided translation

7
Machine translation
  • Translation models
  • Transfer model
  • the dog bit my friend
  • Hindi kutte-ne mere dost ko-kata
  • dog my friend bit

8
Machine translation
  • Translation models
  • Transfer model
  • Alter grammatical structure of source language to
    make it adhere to the grammatical structure of
    target language
  • Use transformation rule
  • Analysis process (source)
  • Transfer process (bridge)
  • Generation process (target)
  • Problem each source-target pair will need it own
    unique set of transformation rules

9
Machine translation
  • Translation models
  • Inter-lingua model
  • Extract the meaning from the source string
  • Give it a language independent representation,
    i.e. an interlingua
  • Translation process takes the interlingua as its
    input
  • Multiple translation processes take the same
    input for multiple target language outputs

10
Machine translation
  • Translation models
  • What is the inter-lingua?
  • for words, some sort of semantic analysis,
  • e.g. (GO, BY-FOOT) (GO, BY-TRANSPORT)
  • Russian ???? ?????
  • English go go

11
Machine translation and globalisation
  • Translation models
  • What is the inter-lingua?
  • for sentences, a logical language
  • e.g. First Order Predicate Calculus

12
Meaning representation
  •  
  • Goal
  • 1. the semantic representation must give you a
    one-to-one mapping to non-linguistic knowledge of
    the world
  • 2. The representation must be expressive, i.e.
    handle different types of data

13
Meaning representation
  •  
  • First Order Predicate Calculus
  • computationally tractable
  • objects (terms)
  • properties of objects
  • relations amongst objects
  • Predicate argument structure
  • large composite representations
  • logical connectives

14
Meaning representation
  •  
  • First Order Predicate Calculus
  • Object referred to uniquely by a term
  • constant e.g. SurreyUniversity
  • function e.g. LocationOf(SurreyUniversity)
  • variable

15
Meaning representation
  •  
  • First Order Predicate Calculus
  • Relations amongst objects
  • Predicates
  • symbols that refer to, or name, the relations
    that hold among some fixed number of objects (J
    M)
  • Educates(SurreyUniversity, Citizens)
  • two-place predicate

16
Meaning representation
  •  
  • First Order Predicate Calculus
  • Relations amongst objects
  • Predicates
  • Can specify the category of an object
  • University(SurreyUniversity)
  • one-place predicate

17
Meaning representation
  •  
  • First Order Predicate Calculus
  • properties / parts of objects
  • functions
  • LocationOf(SurreyUniversity)

18
Meaning representation
  •  
  • First Order Predicate Calculus
  • Composite representations through predicates and
    functions
  • Near(LocationOf(SurreyUniversity),
    LocationOf(Cathedral))

19
Meaning representation
  •  
  • First Order Predicate Calculus
  • Logical connectives
  • combine basic representations to form larger more
    complex representations
  • e.g ? operator and

20
Meaning representation
  •  
  • First Order Predicate Calculus
  • Logical connectives
  • combine basic representations to form larger more
    complex representations
  • Educates(SurreyUniversity, Citizens) ?
  • Remunerates(SurreyUniversity, Staff)

21
Machine translation and globalisation
  •  
  • Machine translation and globalisation change of
    priorities
  • 1954 IBM and Georgetown University, first MT
    demo
  • goal perfect translation
  • 1967 Automatic Language Process Advisory
    Committee (ALPAC) report damning of goal
  • Post ALPAC
  • Goal rough translation, involve human element
  • Current situation online translation, e.g. Babel
    Fish, descendant of SYSTRAN whose goal was rough
    translation
  • Journal of Machine Translation
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