Globalisation and machine translation Machine Translation (MT) The ‘decoding’ paradigm Ambiguity Translation models Interlingua and First Order Predicate

Globalisation and machine translation

Machine Translation (MT) The ‘decoding’ paradigm Ambiguity Translation models Interlingua and First Order Predicate

Calculus Human involvement Historical note

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)

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 → синий, голубой (Russian)

many-to-one (hyponyms → hypernym) hill, mountain → Berg (German) learn, teach → leren (Dutch)

Machine translation and globalisation

Ambiguity‘I made her duck’

“The possibility of interpreting an expression in two or more distinct ways”

Collins English Dictionary

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

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

Machine translation

Translation models Transfer model ‘the dog bit my friend’

Hindi: kutte-ne mere dost ko-kata dog my friend bit

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

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

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: идти ехать

English: go go


Translation models What is the inter-lingua?

for sentences, a logical languagee.g. First Order Predicate Calculus

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

Meaning representation First Order Predicate Calculus

computationally tractable objects (terms) properties of objects relations amongst objects

Predicate argument structure large composite representations

logical connectives


Object: referred to uniquely by a term constant e.g. SurreyUniversity function e.g. LocationOf(SurreyUniversity) variable


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


Relations amongst objects Predicates: Can specify the category of an object

University(SurreyUniversity) one-place predicate


properties / parts of objects functions:

LocationOf(SurreyUniversity)


Composite representations through predicates and functions:Near(LocationOf(SurreyUniversity), LocationOf(Cathedral))


Logical connectives combine basic representations to form

larger more complex representationse.g ٨ operator = ‘and’


Logical connectives combine basic representations to form larger

more complex representationsEducates(SurreyUniversity, Citizens) ٨ ¬ Remunerates(SurreyUniversity, Staff)


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

Documents

Globalisation and machine translation Machine Translation (MT) The ‘decoding’ paradigm Ambiguity Translation models Interlingua and First Order Predicate