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Human Language Technology. Machine Translation Key Issues. Outline. MT = Machine Translation Why MT is important What MT is and why MT is difficult MT and the Human Translator. Why Machine Translation is Important. Implications of Multilinguality. Commerical Interest.

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human language technology

Human Language Technology

Machine Translation

Key Issues

outline
Outline
  • MT = Machine Translation
  • Why MT is important
  • What MT is and why MT is difficult
  • MT and the Human Translator

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commerical interest
Commerical Interest
  • US has invested in MT for intelligence purposes
  • MT is popular on the web - the most used of Google's special features
  • EU spends more that €1B per annum on translation

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academic interest
Academic Interest
  • Different NL technologies include
    • parsing
    • generation
    • morphology
    • pronoun resolution
    • understanding ...

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facts about mt
Facts about MT
  • There are many situations where the ability to less than perfect translations is valuable.
  • MT systems can take over some of the boring, repetitive translation jobs
  • ... and allow human translation to concentrate on more interesting specialist tasks.
  • Building an MT system is complex.

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the place for mt
The Place for MT
  • Human Translators are good at:
    • Getting the right turn of phrase
    • Preserving translation equivalence
  • Human Translators are bad at
    • Dictionary look-up
    • Consistency of translation
    • Translation of terminology
  • MT can exploit these weaknesses

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summary
Summary

MT is important because

  • There are too few human translators
  • Availability of materials in appropriate language has significant economic consequences.
  • Scientifically, it is still one of the best test areas for language technology

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what makes mt hard
What Makes MT Hard
  • Style and Meaning
  • Word Order
  • Word Sense
  • Pronouns
  • Tense
  • Idioms

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style and meaning
As recently as a decade ago it was widely believed that infectious disease was no longer much of a threat in the developed world. The remaining challenges to public health there, it was thought, stemmed from noninfectious conditions such as cancer, heart disease and degenerative diseases.

Il y a une dizaine d’annees, on croyait que les pays industrialises etait debarasses des risques lies aux maladies infectieuses et que la sante publique n’etait menacee que par des maladies comme le cancer, les troubles cardiaques, et les anomolies genetiques

Style and Meaning

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style and meaning1
English

Two sentences

infectious disease was no longer much of a threat in the developed world

The remaining challenges to public health there

noninfectious conditions

French

One sentence

les pays industrialises etait debarasses des risques lies aux maladies infectieuses

la sante publique n’etait menacee que

maladies

Style and Meaning

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different word orders
Different word orders
  • English word order is subject - verb - object
  • Japanese order is subject - object - verb
    • English: IBM bought Lotus
    • Japanese: IBM Lotus bought
    • English: Reporters said IBM bought Lotus
    • Japanese: Reporters IBM Lotus bought said

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word sense ambiguity
Word Sense Ambiguity
  • Bank as in river
  • Bank as in financial insitution
  • Plant as in tree
  • Plant as in factory
  • Different senses usually translate into different words

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different cultural models
Different Cultural Models

English: Health Insurance

German: Krankenversicherung

French: Assurance Maladie

English: validate

French: obliterer

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differences in marking of semantic information
Differences in Marking of Semantic Information
  • Head marking.
    • In English possessive relation is marked on the head: The man's house
    • In Hungarian it is marked on the dependent:The man house-his
    • his house / sa maison
  • Direction and manner of motion marking
    • He ran into the room (English)
    • He entered the room running (French)

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summary1
Summary
  • Translation is about more than equivalence of meaning.
  • Translation may involve the resolution of ambiguity.
  • Preservation of intention involves cultural background as well as linguistic knowledge.
  • Translation is a hard problem – for humans let alone machines.

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similarities and differences between languages
Differences

Morphology

Word order and syntactic structures

Marking of semantic distinctions

Lexical

Similarities

Communicative function for survival

Mechanisms for reference to people, eating, politeness, time.

Syntactic complexity

Nouns

Verbs

Similarities and Differences Between Languages

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in the beginning was the dream of famt
In the Beginning ....was the dream of FAMT
  • Fully Automatic (High Quality) Machine Translation (Bar Hillel 1960)

Source Language text

FAHQMT

TargetLanguage text

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slide24
FAMT
  • Basic Charactistics
    • No human intervention
    • Arbitrary text
  • Evaluation Criteria
    • Quality of ouput
    • Cost ($/page)
    • Speed (pages/hour)

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famt success story taum meteo
FAMT Success StoryTAUM METEO
  • Written by Chevalier et al. 1978.
  • Translation of weather reports from English to French
  • Highly constrained subset of English:
    • Small number of senses for each word
    • Restricted syntactic constructions
  • System determines whether a given sentence is within its capabilities
  • Very fast, very accurate, no post-editing

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famt moral
FAMT: MORAL
  • FAMT can work well but only if we give up one or more of the goals e.g.
    • Unrestricted text input
    • High quality translation
  • This observation has lead to research on sub-languages
  • And to the use of FALQT

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famt is not the only way
FAMT is not the only way
  • FAMT lies at one extreme of a continuum of ways in which technology can be brought to bear upon the translation problem
  • At the other extreme there are word processing software, fax machines, and even mobile phones
  • Between these two extremes there are other points of interest where technology can radically affect the productivity of the individual translator.

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maht and hamt
MAHT and HAMT
  • Machine Aided Human Translation (MAHT)
  • Human Aided Machine Translation (HAMT).
  • The essential difference between these two lies not only in the way in which the person is involved but also in the extent of their involvement

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maht translation memories
MAHT - Translation Memories
  • Systems consist of a database in which each source sentence of a translation is stored together with the target sentence (this is called a translation memory "unit")
  • Any new source sentences will be searched for in the database and a match value is calculated.
  • When the match value is 100%, the translation of the source sentence from the database is inserted into the text being translated.

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maht translation memories1
MAHT - Translation Memories
  • If the match value is below 100% and above a certain user-definable percentage (i.e., "fuzzy match"), the old translation will be inserted as a translation proposal for the translator to review and edit.
  • Sentences with match values below that margin have to be translated from scratch.
  • New and changed translation proposals will then be stored in the database for future use.

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maht translation memories advantages
MAHT - Translation Memories – Advantages
  • Avoid redoing translation of repeated material
  • Use previous texts as a model for new translations
  • Ensure consistency throughout a translation

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maht translation memories drawbacks
MAHT - Translation Memories - Drawbacks
  • If terminology changes between projects the content of a TM needs to be updated to reflect these changes.
  • Blind faith in exact matches (without validation) can generate incorrect translation since there is no verification of the context where the new segment is used compared to where the original one was used.

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maht translation memories remarks
MAHT - Translation Memories - Remarks
  • Translation Process: TM tools may not easily fit into existing translation or localization processes: work best where work can be signed off in pieces rather than as a whole.
  • Customisation: rarely works straight out of the box. Menu adaptation, filters to desktop applications may require significant effort.
  • Investment costs are high
  • Setup and maintenance of TMs has to factored in.
  • OpenTag/TMX formats for exchanging TM data between competing systems

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maht other technology
MAHT – Other Technology
  • Communication/coordination amongst translators
  • Integration of internet technologies and web services.
  • Database technology, smart indexing, and networking
  • Improvements can be achieved that are well within the scope of current technology.

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hamt human assisted machine translation
HAMT – Human Assisted Machine Translation
  • Machine retains the initiative but works in collaboration with human consultant.
  • System translates autonomously until it recognises that a linguistic difficulty of a certain type has arisen, e.g.
    • ambiguity
    • pronoun reference
    • unknown word
    • unrecognised construction
  • At this point it seeks help from the consultant.

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hamt challenges
HAMT – Challenges
  • Reliable identification/classification of difficulty.
  • Reliable communication of difficulty to user.
  • Tradeoff between quality and scope of translation.

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hamt advantages
HAMT - Advantages
  • Modulo challenges – a high quality of translation can be guaranteed.
  • Speed – if large sections of text can be translated automatically.
  • Human consultant need not necessarily have all the skills of a human translator; native competence in one or both languages may suffice.

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summary2
Summary
  • Machine Translation is a continuum
    • FAMT
    • HAMT
    • MAHT
  • The utility of a given type of system cannot be assessed with very simple criteria
  • Utlility function involves at least the human cost, the machine cost, the quality of the result, and the nature of the translation requirements.

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some references
Some References
  • Jonathan Slocum, Machine Translation: its History, Current Status, and Future Prospects, Proc ACL 1984, Stanford University, http://acl.ldc.upenn.edu/P/P84/P84-1116.pdf
  • Martin Kay – Machine Translation, Computational Linguistics vol 11 numbers 2-3 1985.
  • Richard Kittredge – Sublanguages, Computational Linguistics vol 11 numbers 2-3 1985.

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