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Machine Translation (Level 2)

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Machine Translation (Level 2). Anna Sågvall Hein GSLT Course, September 2004. Translation. ”substitute the text material of one language (SL) by the equivalent text material of another language (TL)” (Catford 1965: 20) - PowerPoint PPT Presentation
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Machine Translation (Level 2) Anna Sågvall Hein GSLT Course, September 2004
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Page 1: Machine Translation (Level 2)

Machine Translation (Level 2)

Anna Sågvall Hein

GSLT Course, September 2004

Page 2: Machine Translation (Level 2)

Translation

”substitute the text material of one language (SL) by the equivalent text material of another language (TL)” (Catford 1965: 20)

”Translation consists in producing in the target language the closest natural equivalent of the text material of the source language, in the first hand concerning meaning, in the second hand concerning style (Nida 1975: 32)

”Translation is in theory impossible, but in practice fairly possible” Mounin (1967)

Catford, J. C. (1965), A Linguistic Theory of Translation, Oxford Press, England.Mounin, G. (1967) Les problèmes théotitiques de la traduction. ParisNida, E. (1975), A Framework for the Analysis and Evaluation of Theories of

Translation, in Brislin, R. W. (ed) (1975), Translation Application and Research, Gardner Press, New York.

Page 3: Machine Translation (Level 2)

Equivalence

• form

• meaning

• style

• effect

Page 4: Machine Translation (Level 2)

Formal and dynamic equivalence

• Formal equivalence focuses attention on the message itself, in both form and content. It aims to  allow the reader to understand as much of the SL context as possible.

• Dynamic equivalence is based on the principle of equivalent effect, i.e. that the relationship between receiver and message should aim at being the same as that between the original receivers and the SL message.

(Nida 75)

Page 5: Machine Translation (Level 2)

Can computers translate?

• Not a simple yes or no; it depends on the purpose of the translation and the required quality.

Page 6: Machine Translation (Level 2)

Classical problems with MT

• unrealistic expectations

• bad translations

• difficulties in integrating MT in the work flow– the Ericsson case

Page 7: Machine Translation (Level 2)

Feasibility of machine translation

• quality in relation to purpose

• control of the source language

• human machine interaction

• re-use of translations

• evalution

Page 8: Machine Translation (Level 2)

Quality

• publishing quality

• editing quality

• browsing qualiy

Page 9: Machine Translation (Level 2)

Translation related tasks

• translation

• browsing

• gisting

• drafting

• message dissemination

• cross-language information searches

• cross-language interchanges

Page 10: Machine Translation (Level 2)

MT as a cross-language communication tool

MT is used not only for pure translation purposes but also for writing in a foreign language and for browsing (Hutchins 2001)

Hutchins, J., 2001, Towards a new vision for MT, Introductory speech at MT Summit VIII conference, 18-22 September 2001

(http://ourworld.compuserve.com/homepages/WJHutchins/MTS-2001.pdf)

Page 11: Machine Translation (Level 2)

Control of the source language

• spell checked and grammar checked SL

• sublanguage– Domain– Text type

• controlled language

Page 12: Machine Translation (Level 2)

Spell checking and grammar checking

• If there are spelling errors or typos in the SL dictionary search will fail

• If there are grammatical errors in the SL grammatical analysis will fail

• Where and how should spell and grammar checking be accounted for? Before or in the process?

Page 13: Machine Translation (Level 2)

Controlled language

• consistent authoring of source texts– reduction of ambiguity– full linguistic coverage

• controlled vocabulary– full lexical coverage

• controlled grammar– full grammatical coverage

• controlled language checking– e.g. Scania Checker

Page 14: Machine Translation (Level 2)

Ex. of controlled languages

• Simplified English

• KANT controlled English

• Scania Swedish– Scania checker

Page 15: Machine Translation (Level 2)

Human intervention

• before– language checking

• during– e.g. ambiguity resolution

• after– post-editing

Page 16: Machine Translation (Level 2)

Re-use of translations

• translation memories

• translation dictionaries incl. terminologies

• lexicalistic translation

• statistical machine translation

• example-based translation

Page 17: Machine Translation (Level 2)

Evaluation of MT

• human

• automatic – using a gold standard

• coverage (recall)• quality (precision)• global similarity measures

– merge of recall and precision– BLEU, NIST

Page 18: Machine Translation (Level 2)

Why machine translation?

• cheaper

• faster

• more consistent– when it succeeds …

Page 19: Machine Translation (Level 2)

What is MT proper?

To be considered as MT, a system should provide• minimally correct morphology• minimal syntactic processing• minimal semantic processing• handle and produce full sentences

Hutchins, J., 2000, The IAMT Certification initiative and defining translation system categories (http://nl.ijs.si/eamt00/proc/Hutchins.pdf)

Page 21: Machine Translation (Level 2)

Basic strategies

• direct translation

• rule-based translation– transfer– interlingua

• example-based translation

• statistical translation

• hybrids


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