Making machine translation work

1
Making machine translation work

• By Stefan, Simon, Lisa, Nina and Dennis

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Making machine translation work

• Introduction
• Human versus Machine Translation
• Methods in Machine Translation
• Example-Based Machine Translation

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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)

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Making machine translation work

• Possible solution

HT MT
In for a penny, in for a pound. Who says A, also B must say.
In how far is such a translation
suitable/appropriate?
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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

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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

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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

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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

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Human and Machine Translation- translation
processes -

• Translation as problem solving

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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

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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

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Human and Machine Translation - translation
processes -

• MT model of problem solving

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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

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Human and Machine Translation - translation
processes
Major levels of comparison
Human modules Machine modules
Comprehension Analysis
Matching Transfer
Writing Generation/Synthesis
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Human and Machine Translation - translation
processes

• Comprehension vs. Analysis

Human Machine
adapts innovations works retrospectively
high amount of interpretative capacity limited amount of interpretative capacity
interferencing
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Human and Machine Translation - translation
processes

• Matching vs. Transfer

Human Machine
compensation of items which cannot be matched in conventional ways equivalents cannot be pre-planned or incorporated
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Human and Machine Translation - translation
processes

• Writing vs. Generation/Synthesis

Human Machine
can respond to syntactic or lexical innovations or deviations works prospectively
can create equivalences
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Human and Machine Translation- translation
products -

• Products can be compared with regard to
• to the nature of the output language
• to the produced text

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Human and Machine Translation- translation
products -

• The nature of MT language
• MT language is constructed and artificial (the
  computer cant produce sentences on its own)
• it corresponds to the designers 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)

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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

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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

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Human and Machine Translation- translation
products -
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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

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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

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Methods of MT Linguistic approach

• three strategies
• Analysis of the source text
• Mode of transfer
• Generation of target text

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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

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Linguistic approachThree main subtypes

• b) Interlingual systems
• 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.

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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.

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Machine assisted human translation
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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

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Machine assisted human translation
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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

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Machine assisted human translation
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Machine assisted human translation

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

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Machine assisted human translation Three-stage
machine assistance
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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

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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

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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

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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

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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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Reader-oriented MT

• Readers accept difficult-to-read texts if they
  are cheap and above all fast

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Reader-oriented MT

• Output is machine-produced and therefore by
  definition an artificial product which may be
  easier or more difficult to understand than a NL
  text
• Not comparable to a human translation
• L2 reader receive a text in L1
• Submit the text to MT in full knowledge that the
  output is a machine-translated text

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Writer-oriented MT

• Writer know better than anybody else what they
  want to say
• Translators have to interpret what writers have
  said
• Machine asks questions about elements which it
  cannot analyse

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Writer-oriented MT
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Writer oriented editing of pre-translated text

• System offer menus of existing SL text segments
  which are pre-translated
• E.g. business letters choice of type of letter,
  separate menus within the types

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EBMT
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Definition

• Man does not translate a simple sentence by
  doing deep linguistic analysis, rather, man does
  translation, first, by properly decomposing an
  input-sentence into other language phrases, and
  finally by properly composing these fragmental
  translations into one long sentence. The
  translation of each fragmental phrase will be
  done by the analogy translation principle with
  proper examples as its reference. (Nagao)

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Model EBMT
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• EMBT does not presuppose an analytic translation
• ? it is an analog translation system
• Founded on
• 1) translation by decomposing
• 2) translation of phrases
• 3) composing fragments into long section

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• EBMT consists of a bilingual corpus
• 1) a fixed corpus (how much is) of
  sentence-pairs
• Example How much is the bread? Wie teuer ist
  das Brot?
• How much is the car? Wie teuer ist das Auto?
• ?question varies by just one element (minimal
  pair the bread/the car)

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• Often linked with translation memory (TM)
• it must in fact be possible to produce a
  programme, which would enable the word processor
  to remember whether any part of a new text
  typed into it had already been translated.
• ? T9-typing within mobile phones

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History

• Until the 80s ? rule-based translations
• Research dominated by corpus-based approaches
• 1) statistical machine translation
• 2) EBMT
• first suggested by Nagao Makoto in 1984
• soon attracted the attention of scientists in the
  field of natural language processing.

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Matching

• First task in an EBMT system
• Searched for a word or phrase that closely
  matches the source language
• ? Example Where is the plate
• ? Correct translation Wo ist der Teller
• ? and not Wo ist die Platte
• most appropriate word is inserted

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Problems

• Long passages ? low probability of complete match
• Short passages ? probability of ambiguity
• Sentences are not translated completely but are
  divided into smaller sections
• ? often incoherent translation results

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• Problem of the size of the example database
• Some of the systems are more experimental than
  others
• Adding examples improves translation performance
• No improvement after an amount of examples, which
  is too broad

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Problem suitability of examples

• Some examples have identical translation
• Same phrase may have two different translations
  caused by inconsistency
• Too big variety of examples may cause problems
  with the choice of the exact word
• Ambiguity
• Can lead to overgeneralization

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Problem storage of examples

• Normally words are stored with no further
  information
• To avoid ambiguity and to limit the choice
• ?Expansion of examples by adding contextual
  markers
• ?context is regarded in order to help finding the
  right word

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Suitable translation problems

• EBMT best suited for sublanguage translation
• EBMT is often more suitable than MT
• Antidote to structure-preserving translation as
  first choice

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Adaptability

• Most difficult step in EBMT process
• Appropriate fragments have to be extracted from
  the text
• Problem 1) words have to find its correspondence
  to the matched portions
• 2) find the correct
  recombination, which is appropriate and
  grammatical

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Boundary Friction Problem of inflection

• ?Example I ate the apple
• Translation Ich aß der Apfel
• Example II The apple is on the table
• Translation Der Apfel liegt auf dem Tisch.
• To solve the problem the translation system had
  to contain a grammatical system of the target
  language

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• Examples should be similar in internal and
  external context
• Example-retrieval can be scored on two counts
• ? closeness of the match between the input text
  and the example
• ? the adaptability of the example, on the basis
  of the relationship between the representations
  of the example and its translations

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Recombination

• New generation of the target text
• Last action in translation-process
• Often not possible to put translated phrases
  together
• ?Example Its raining outside Es regnet nach
  draußen
• Recombination has to make sure that the phrases
  are put together conformly
• ?Example Its raining outside Es regnet
  draußen

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Computational Problems

• Huge costs in terms of
• ?storage
• ?creation
• ?matching/retrieval algorithms
• SPEED as a main issue
• ? A computer-translation has to be as fast as a
  speech-translation

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Flavours of EBMT

• Used as a component in a MT-system
• EBMT can be used
• ?with other engines
• ?for certain problems
• ?when some other component cannot deliver a
  result
• EBMT bitter rival to the existing engines

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Example-based transfer

• Examples are stored as trees or other complex
  structures as example-based transfer systems.
• ? In these systems, source language input
  strings are analysed into structured
  representations in a conventional manner, only
  transfer is on the basis of examples rather than
  rules, and then generation of the target language
  output is again done in a traditional way. (H.
  Somers)

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Generalization

• Syntactic category
• Example
• ?play baseball yakyu o suru
• ?play tennis tenisu o suru
• ?play the piano piano o hiku
• ?play the violine baiorin o hiku
• Different vocabulary for play in Japanese,
  engine has to distinguish whether an instrument
  or sport is meant
• Play x (NP/sport) x (NP) o suru
• Play x (NP/instrument) x (NP) o hiku

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Generalization

• Syntactic category
• Example
• ?play baseball yakyu o suru
• ?play tennis tenisu o suru
• ?play the piano piano o hiku
• ?play the violine baiorin o hiku
• Different vocabulary for play in Japanese,
  engine has to distinguish whether an instrument
  or sport is meant
• Play x (NP/sport) x (NP) o suru
• Play x (NP/instrument) x (NP) o hiku

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• Semantic category
• A word must be chosen first
• Word is generated
• Word-level rule is made up
• The quality of the translation rules depends on
  the quality of the thesaurus
• Works best with non-idiomatic texts

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• Automatic category
• A simpler approach
• Less initial analysis of the corpora
• ?I am coming geliyorum
• ?I am going gidiyorum
• ?I am comeing gelHyoryHm
• ?I am going gidHyoryHm
• - I am stays fixed, while come and go differ

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Multi-engine system

• EBMT two other techniques knowledge based MT
  and lexical transfer engine
• Multi-engine system combines EBMT with
  rule-based and corpus-based approaches
• User can
• ?modify the results
• ?intervene in the choice of translation
• ?edit the output

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Conclusion

• What counts as EBMT?
• Use of a bilingual corpus
• Use of a reference corpus
• What is the aim of EBMT?
• ?to generalize the examples as much as possible
• What is the problem of EBMT?
• ? Some translations are suitable, some are not

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• Advantages of EBMT
• Examples are real language data overgeneration
  is reduced
• ?Linguistic knowledge can be more easily enriched
  by adding more examples
• ?can be quickly developed
• ?not as a rival but as an alternative

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Literatur

• Somers, H. (2003). An overview of EBMT. In
  Michael Carl and Andy Way (eds) Recent advances
  in Example-Based Machine Translation, Dordrecht
  Kluwer, 3-57.
• Sager, J. (1994). Language engineering and
  translation consequences of translation.
  Amsterdam. 267-292