Knowledgebased Machine Translation KBMT

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Knowledge-based Machine Translation (KBMT)

• 11-682/15-482
• Introduction to IR, NLP, MT and Speech
• December 1, 2005

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Approaches to MT Vaquois MT Triangle
Interlingua
Give-informationpersonal-data (namealon_lavie)
Generation
Analysis
Transfer
s vp accusative_pronoun chiamare proper_name
s np possessive_pronoun name vp be
proper_name
Direct
Mi chiamo Alon Lavie
My name is Alon Lavie
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KBMT Analysis and Generation

• Analysis
• Morphological analysis (word-level) and POS
  tagging
• Syntactic analysis and disambiguation (produce
  syntactic parse-tree)
• Semantic analysis and disambiguation (produce
  logical form representation)
• Map to language-independent Interlingua
• Generation
• Generate semantic representation in TL
• Sentence Planning generate syntactic structure
  and lexical selections for concepts
• Surface-form realization generate correct forms
  of words

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Transfer Approaches

• Syntactic Transfer
• Analyze SL input sentence to its syntactic
  structure (parse tree)
• Transfer SL parse-tree to TL parse-tree (various
  formalisms for specifying mappings)
• Generate TL sentence from the TL parse-tree
• Semantic Transfer
• Analyze SL input to a language-specific semantic
  representation (i.e. logical form)
• Transfer SL semantic representation to TL
  semantic representation
• Generate syntactic structure and then surface
  sentence in the TL

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Transfer Approaches

• Main Advantages and Disadvantages
• Syntactic Transfer
• No need for semantic analysis and generation
• Syntactic structures are general, not domain
  specific ? Less domain dependent, can
  handle open domains
• Requires word translation lexicon
• Semantic Transfer
• Requires deeper analysis and generation, symbolic
  representation of concepts and predicates ?
  difficult to construct for open or unlimited
  domains
• Can better handle non-compositional meaning
  structures ? can be more accurate
• No word translation lexicon generate in TL from
  symbolic concepts

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Interlingua KBMT

• The natural deep Artificial Intelligence
  approach
• Analyze the source language into a language
  independent detailed symbolic representation of
  its meaning
• Generate this meaning in the target language
• Interlingua one single meaning representation
  for all languages
• Nice in theory, but extremely difficult in
  practice

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What is an Interlingua?

• Representation of meaning or speaker intention.
• Sentences that are equivalent for the translation
  task have the same interlingua representation.
• The room costs 100 Euros per night.
• The room is 100 Euros per night.
• The price of the room is 100 Euros per night.

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The Interlingua KBMT approach

• With interlingua, need only N parsers/ generators
  instead of N2 transfer systems

L2
L2
L3
L1
L1
L3
interlingua
L6
L4
L6
L4
L5
L5
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Advantages of Interlingua

• Add a new language easily
• get all-ways translation to all previous
  languages by adding one grammar for analysis and
  one grammar for generation
• Mono-lingual development teams.
• Paraphrase
• Generate a new source language sentence from the
  interlingua so that the user can confirm the
  meaning

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Disadvantages of Interlingua

• Meaning is arbitrarily deep.
• What level of detail do you stop at?
• If it is too simple, meaning will be lost in
  translation.
• If it is too complex, analysis and generation
  will be too difficult.
• Should be applicable to all languages
• how do we ensure that?
• Human development time.

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KBMT KANT, KANTOO, CATALYST

• Deep knowledge-based framework, with symbolic
  interlingua as intermediate representation
• Syntactic and semantic analysis into a
  unambiguous detailed symbolic representation of
  meaning using unification grammars and
  transformation mappers
• Generation into the target language using
  unification grammars and transformation mappers
• First large-scale multi-lingual interlingua-based
  MT system deployed commercially
• CATALYST at Caterpillar high quality translation
  of documentation manuals for heavy equipment
• English (source) to French, Spanish, German
  (target)
• Limited domains and controlled English input
• Minor amounts of post-editing

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Interlingua-based Speech-to-Speech MT

• Evolution from JANUS/C-STAR systems to NESPOLE!,
  LingWear, BABYLON
• Early 1990s first prototype system that fully
  performed speech-to-speech (very limited domain)
• Interlingua-based, but with shallow task-oriented
  representations
• we have single and double rooms available
• give-informationavailability
• (room-typesingle, double)
• Semantic Grammars for analysis and generation
• Multiple languages English, German, French,
  Italian, Japanese, Korean, and others
• Most active work on portable speech translation
  on small devices Arabic/English and Thai/English

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Major Sources of Translation Problems

• Lexical Differences
• Multiple possible translations for SL word, or
  difficulties expressing SL word meaning in a
  single TL word
• Structural Differences
• Syntax of SL is different than syntax of the TL
  word order, sentence and constituent structure
• Differences in Mappings of Syntax to Semantics
• Meaning in TL is conveyed using a different
  syntactic structure than in the SL
• Idioms and Constructions

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Lexical Differences

• SL word has several different meanings, that
  translate differently into TL
• Ex financial bank vs. river bank
• Lexical Gaps SL word reflects a unique meaning
  that cannot be expressed by a single word in TL
• Ex English snub doesnt have a corresponding
  verb in French or German
• TL has finer distinctions than SL ? SL word
  should be translated differently in different
  contexts
• Ex English wall can be German wand (internal),
  mauer (external)

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Lexical Differences

• Lexical gaps
• Examples these have no direct equivalent in
  Englishgratiner(v., French, to cook with a
  cheese coating)otosanrin(n., Japanese,
  three-wheeled truck or van)

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Lexical Differences
From Hutchins Somers
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MT Handling of Lexical Differences

• Direct MT and Syntactic Transfer
• Lexical Transfer stage uses bilingual lexicon
• SL word can have multiple translation entries,
  possibly augmented with disambiguation features
  or probabilities
• Lexical Transfer can involve use of limited
  context (on SL side, TL side, or both)
• Lexical Gaps can partly be addressed via phrasal
  lexicons
• Semantic Transfer
• Ambiguity of SL word must be resolved during
  analysis ? correct symbolic representation at
  semantic level
• TL Generation must select appropriate word or
  structure for correctly conveying the concept in
  TL

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Structural Differences

• Syntax of SL is different than syntax of the TL
• Word order within constituents
• English NPs art adj n the big boy
• Hebrew NPs art n art adj ha yeled ha gadol
• Constituent structure
• English is SVO Subj Verb Obj I saw the man
• Modern Arabic is VSO Verb Subj Obj
• Different verb syntax
• Verb complexes in English vs. in German
• I can eat the apple Ich kann den apfel essen
• Case marking and free constituent order
• German and other languages that mark case
• den apfel esse Ich the(acc) apple eat I(nom)

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MT Handling of Structural Differences

• Direct MT Approaches
• No explicit treatment Phrasal Lexicons and
  sentence level matches or templates
• Syntactic Transfer
• Structural Transfer Grammars
• Trigger rule by matching against syntactic
  structure on SL side
• Rule specifies how to reorder and re-structure
  the syntactic constituents to reflect syntax of
  TL side
• Semantic Transfer
• SL Semantic Representation abstracts away from SL
  syntax to functional roles ? done during analysis
• TL Generation maps semantic structures to correct
  TL syntax

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Syntax-to-Semantics Differences

• Meaning in TL is conveyed using a different
  syntactic structure than in the SL
• Changes in verb and its arguments
• Passive constructions
• Motion verbs and state verbs
• Case creation and case absorption
• Main Distinction from Structural Differences
• Structural differences are mostly independent of
  lexical choices and their semantic meaning ?
  addressed by transfer rules that are syntactic in
  nature
• Syntax-to-semantic mapping differences are
  meaning-specific require the presence of
  specific words (and meanings) in the SL

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Syntax-to-Semantics Differences

• Structure-change example
• I like swimming
• Ich scwhimme gern
• I swim gladly

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Syntax-to-Semantics Differences

• Verb-argument example
• Jones likes the film.
• Le film plait à Jones.
• (lit the film pleases to Jones)
• Use of case roles can eliminate the need for this
  type of transfer
• Jones Experiencer
• film Theme

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Syntax-to-Semantics Differences

• Passive Constructions
• Example French reflexive passivesCes livres se
  lisent facilementThese books read themselves
  easilyThese books are easily read

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Same intention, different syntax

• rigly bitiwgacny
• my leg hurts
• candy wagac fE rigly
• I have pain in my leg
• rigly bitiClimny
• my leg hurts
• fE wagac fE rigly
• there is pain in my leg
• rigly bitinqaH calya
• my leg bothers on me
• Romanization of Arabic from CallHome Egypt.

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MT Handling of Syntax-to-Semantics Differences

• Direct MT Approaches
• No Explicit treatment Phrasal Lexicons and
  sentence level matches or templates
• Syntactic Transfer
• Lexicalized Structural Transfer Grammars
• Trigger rule by matching against lexicalized
  syntactic structure on SL side lexical and
  functional features
• Rule specifies how to reorder and re-structure
  the syntactic constituents to reflect syntax of
  TL side
• Semantic Transfer
• SL Semantic Representation abstracts away from SL
  syntax to functional roles ? done during analysis
• TL Generation maps semantic structures to correct
  TL syntax

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Example of Structural Transfer Rule(verb-argument
)
From Hutchins Somers
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Semantic Transfer Theta Structure (case roles)
From Hutchins Somers

• Abstracts away from grammatical functions
• Looks more like a semantic f-structure
• The basis forsemantic transfer

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Idioms and Constructions

• Main Distinction meaning of whole is not
  directly compositional from meaning of its
  sub-parts ? no compositional translation
• Examples
• George is a bull in a china shop
• He kicked the bucket
• Can you please open the window?

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Formulaic Utterances

• Good night.
• tisbaH cala xEr
• waking up on good
• Romanization of Arabic from CallHome Egypt

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Constructions

• Identifying speaker intention rather than literal
  meaning for formulaic and task-oriented
  sentences.
• How about suggestion
• Why dont you suggestion
• Could you tell me request info.
• I was wondering request info.

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MT Handling of Constructions and Idioms

• Direct MT Approaches
• No Explicit treatment Phrasal Lexicons and
  sentence level matches or templates
• Syntactic Transfer
• No effective treatment
• Highly Lexicalized Structural Transfer rules
  can handle some constructions
• Trigger rule by matching against entire
  construction, including structure on SL side
• Rule specifies how to generate the correct
  construction on the TL side
• Semantic Transfer
• Analysis must capture non-compositional
  representation of the idiom or construction ?
  specialized rules
• TL Generation maps construction semantic
  structures to correct TL syntax and lexical words

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Transfer-based MT Systems

• Primarily Syntactic-transfer, based on large
  manually developed transfer grammars
• Most notable systems
• SYSTRAN translation engines
• PAHO system (Spanish/English)
• EUROTRA
• VERBMOBIL
• Main Issues
• Large volume and complexity of transfer grammars
• Interaction between general and exception
  rules
• Interaction between transfer grammar and lexicon

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