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Making machine translation work

Making machine translation work. By Stefan, Simon, Lisa, Nina and Dennis. Making machine translation work. Introduction Human versus Machine Translation Methods in Machine Translation Example-Based Machine Translation. Making machine translation work. Group work: HT vs. MT

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Making machine translation work

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  1. Making machine translation work By Stefan, Simon, Lisa, Nina and Dennis

  2. Making machine translation work • Introduction • Human versus Machine Translation • Methods in Machine Translation • Example-Based Machine Translation

  3. Making machine translation work • Group work: HT vs. MT • Try to translate the following proverb: → “Wer A sagt, muss auch B sagen.” • HT: use your language knowledge • MT: Use Babel Fish (http://babelfish.altavista.com/tr)

  4. Making machine translation work • Possible solution: In how far is such a translation suitable/appropriate…?

  5. Human and Machine Translation • HT and MT differ in two main points: • 1. Mode of process • 2. Mode of product • based on different specifications and theoretical positions • both modes are used for comparison

  6. Human and Machine Translation Mode of process • By comparing the modes of process you: • gain knowledge about the respective stages and intersections • can make decisions about choices of alternative methods • … and about new designs of translation methods

  7. Human and Machine Translation Mode of product • By comparing the modes of product you: • check the appropriateness of the translation • figure out the most efficient method → the MT product must be usable in the same way as the human product → secure a basis of equality

  8. Human and Machine Translation Another criterion for comparison: • text input must be a constant so that the products are comparable • → help to formulate guidelines for HT or MT texts

  9. Human and Machine Translation- translation processes - Translation as problem solving

  10. Human and Machine Translation - translation processes - • Four major steps: • (a ) SL linguistic de-composition • (b) Problem identification at the SL linguistic and cognitive level • (c) Problem solution at the cognitive and TL linguistic level (knowledge base) • (d) TL linguistic re-composition

  11. Human and Machine Translation - translation processes - • Characteristics of HT: • Knowledge base is flexible • Problems can be transferred • Intuition/experience of the translator • Knowledge base expands constantly

  12. Human and Machine Translation - translation processes - MT model of problem solving

  13. Human and Machine Translation - translation processes - • Characteristics of MT: • Knowledge base is relatively limited and rigid • Has fixed and pre-established connections • Limited possibility of transferring problems • less semantic and pragmatic level experience • Lack of essential world-knowledge

  14. Human and Machine Translation - translation processes – Major levels of comparison

  15. Human and Machine Translation - translation processes – • Comprehension vs. Analysis

  16. Human and Machine Translation - translation processes – • Matching vs. Transfer

  17. Human and Machine Translation - translation processes – • Writing vs. Generation/Synthesis

  18. Human and Machine Translation- translation products - • Products can be compared with regard to • to the nature of the output language • to the produced text

  19. Human and Machine Translation- translation products - The nature of MT language • MT language is constructed and artificial (the computer can’t produce sentences on its own) • it corresponds to the designer’s perception of SL and TL • has no creative potential (it is not as flexible and multifunctional as HT language) • They exclude emotive, aesthetic of other meanings → each MT system produces its own language (i.e. Weidner English or Atlas English)

  20. Human and Machine Translation- translation products - The nature of MT language • MT systems are one-way converter (they only recognize words that belong to the system) • MT language often needs post-editing

  21. Human and Machine Translation- translation products - Flexibility vs. rigidity in text types • MT lang. is conceived on the sentence level → no distinctions on the text type possible → MT systems can only handle text types they have been programmed for → unknown text types cause unacceptable output

  22. Human and Machine Translation- translation products -

  23. Human and Machine Translation- translation products - Challenge for MT language • construction of user-friendly articifial language • optimum transfer of information from SL/NL to AL • to convince users that AL is equally efficient as NL

  24. The Pragmatic Circumstances of Automation in Translation • Methods of MT • Linguistic approach • Semantic approach • Users of MT systems • Some MT systems • Functional types of MT

  25. Methods of MT Linguistic approach • three strategies: • Analysis of the source text • Mode of transfer • Generation of target text

  26. Linguistic approachThree main subtypes • a) Language-pair-specific “direct” systems • Earliest type of system • Reflects the design philosophy of the 1950s and 1960s • Exploited direct correspondences between two languages

  27. Linguistic approachThree main subtypes b) Interlingualsystems • SL text transformed into a semantic and syntactic representation (equivalent of the transfer phase) which is common to at least two languages • That text in an other language can be generated from this representation • “transform from a source language A into a target language B, using rules expressed in a third language C”. (Cherry. 1966) • Two phases: 1. Analysing in terms of the interlingual representation 2. TL sentences are produced from this representation.

  28. Linguistic approachThree main subtypes c) Transfer systems • Analysis phase: SL text is processed to the depth required by the rules of its grammar • Transfer phase: based on the target language transforming into a representation for the generation of a target language text • Generation phase: the transfer representation is then transformed into a text in the TL without any further back-reference to the results of analysis.

  29. The semantic approach • Semantic processes only operate after the identification of syntactic structures. • Chief components are semantic parsing, i.e. analysis of semantic features instead of, or in addition to, grammatical categories. • The system does “understand” the SL text, before translation begins.

  30. Users of MT systems The translator as producer • Machine to provide cheaper, faster and a larger volume of production, without significant loss of quality • Clearly seen as a industry product

  31. Users of MT systems The writer as translation producer • Writers gain a certain degree of independence from translators, who exclusively determined form and quality of the end product • Writers may want to develop bi- or multilingual texts directly rather than write a text for subsequent translation

  32. Users of MT systems Readers of translation • to be able to by-pass the time-consuming and costly human translation circuit, and instead obtain instant translations produced by an MT system

  33. Users of MT systems The information supplier • possibilities of providing translated versions automatically as part of the general information supply, e.g. multilingual versions of electronic journals or databases

  34. Some MT systems • ATLAS • Japanese system, based on structural transfer, for specialised technical texts • CULT • Interactive system, for on-line translation of texts in the field of mathematics from Chinese into English

  35. Some MT systems • METEO • The Canadian Federal Government system for the production of bilingual French-English weather reports • SYSTRAN • Oldest commercially available MT system, of un-edited output, for post-editing use, for restricted-language document input and for general use in the French Minitel system • Largest number of language pairs, all EC languages

  36. Function types of machine translation • Two possible modes of viewing automatic translation: • See the computer as an aid to human translation • Accept that the computer provides a translation service sui generis which is not comparable to the human variety

  37. MT as human translation aid • MT as aids to translators • Intended to accelerate the human process of translation • Output is artificial to the extend that it does not conform to certain expectations • End user still wants a human product, but will accept MT as long as it is either cheaper or produced more quickly

  38. MT as human translation aid • Systems are greatly improved by concentrating on particular text types and ranges of vocabulary • Systems offer subject-specific modules of vocabulary and phraseology that can be switched into the process

  39. Machine assisted human translation

  40. Machine assisted human translation • Check text against an automated dictionary • Ignores common words and function words • Looks up translation equivalents for special vocabulary items • Speed up the process

  41. Machine assisted human translation

  42. Machine assisted human translation • Text is pre-translated automatically • Output not adequate for direct use or post-editing • Offers words and expressions • Translator reduce the time for dictionary look-up • Save the time of actually typing the found translation equivalents

  43. Machine assisted human translation

  44. Machine assisted human translation • MT produces artificial language (AL2) • Post-editing efforts must be less than that required for a full human translation

  45. Machine assisted human translation Three-stage machine assistance

  46. Machine assisted human translation • Text is prepared for MT by human pre-editing • System produces output in AL2 which post-editors can convert into a NL2 document • Final document is not distinguishable from a human translation

  47. Machine assisted translation • These models of MT hide the true nature of MT • Rather an aid than an alternative to human translation • Application is limited • simplest and the most difficult types of MT systems to design • Examples: ALPS, ATLAS, WEIDNER, SYSTRAN

  48. Translation by reference to existing models • System scans existing documents by text-deconstruction method of text comparison • Identifies similar passages and offer these to the translator as models for the new task

  49. MT as text-type specific independent systems • ‘automatic’ in the sense that human intervention is not required between input and output • Is used • Without the intervention of a human translator • As a text-production system for previously edited

  50. MT as text-type specific independent systems • Three forms of output: • Raw translation in AL2 suitable for post-editing and possible conversion to NL2 • A final AL2 version which can be used almost in same way as natural language text, has been pre-editing • Unedited final translation, i.e. an artificial language, which is acceptable for readers

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