AVENUE/LETRAS: Learning-based MT Approaches for Languages with Limited Resources

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AVENUE/LETRASLearning-based MT Approaches for
Languages with Limited Resources

• Alon Lavie, Jaime Carbonell, Lori Levin,
• Bob Frederking
• Joint work with
• Erik Peterson, Christian Monson, Ariadna
  Font-Llitjos, Alison Alvarez, Roberto Aranovich

2
Why Machine Translation for Languages with
Limited Resources?

• We are in the age of information explosion
• The internetwebGoogle ? anyone can get the
  information they want anytime
• But what about the text in all those other
  languages?
• How do they read all this English stuff?
• How do we read all the stuff that they put
  online?
• MT for these languages would Enable
• Better government access to native indigenous and
  minority communities
• Better minority and native community
  participation in information-rich activities
  (health care, education, government) without
  giving up their languages.
• Civilian and military applications (disaster
  relief)
• Language preservation

3
AVENUE/LETRAS Funding

• Started in 2000 with small amount of DARPA/TIDES
  funding (NICE)
• AVENUE project funded by 5-year NSF ITR grant
  (2001-2006)
• Follow-on LETRAS project funded by NSF HLC
  Program grant (2006-2009)
• Collaboration funding sources
• Mapudungun (MINEDUC, Chile)
• Hebrew (ISF, Israel)
• Brazilian Portuguese Native Langs. (Brazilian
  Gov.)
• Inupiaq (NSF, Polar Programs)

4
CMUs AVENUE Approach

• Elicitation use bilingual native informants to
  create a small high-quality word-aligned
  bilingual corpus of translated phrases and
  sentences
• Building Elicitation corpora from feature
  structures
• Feature Detection and Navigation
• Transfer-rule Learning apply ML-based methods to
  automatically acquire syntactic transfer rules
  for translation between the two languages
• Learn from major language to minor language
• Translate from minor language to major language
• XFER Decoder
• XFER engine produces a lattice of possible
  transferred structures at all levels
• Decoder searches and selects the best scoring
  combination
• Rule Refinement refine the acquired rules via a
  process of interaction with bilingual informants
• Morphology Learning
• Word and Phrase bilingual lexicon acquisition

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AVENUE MT Approach

• Interlingua

Semantic Analysis
Sentence Planning
Syntactic Parsing
Text Generation
Transfer Rules
AVENUE Automate Rule Learning
Source (e.g. Quechua)
Target (e.g. English)
Direct SMT, EBMT
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AVENUE Architecture
Elicitation
Rule Learning
Run-Time System
Rule Refinement
Morphology
Translation Correction Tool
Word-Aligned Parallel Corpus
INPUT TEXT
Run Time Transfer System
Rule Refinement Module
Elicitation Corpus
Decoder
Elicitation Tool
Lexical Resources
OUTPUT TEXT
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Transfer Rule Formalism
SL the old man, TL ha-ish ha-zaqen NPNP
DET ADJ N -gt DET N DET ADJ ( (X1Y1) (X1Y3)
(X2Y4) (X3Y2) ((X1 AGR) 3-SING) ((X1 DEF
DEF) ((X3 AGR) 3-SING) ((X3 COUNT)
) ((Y1 DEF) DEF) ((Y3 DEF) DEF) ((Y2 AGR)
3-SING) ((Y2 GENDER) (Y4 GENDER)) )

• Type information
• Part-of-speech/constituent information
• Alignments
• x-side constraints
• y-side constraints
• xy-constraints,
• e.g. ((Y1 AGR) (X1 AGR))

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Transfer Rule Formalism (II)
SL the old man, TL ha-ish ha-zaqen NPNP
DET ADJ N -gt DET N DET ADJ ( (X1Y1) (X1Y3)
(X2Y4) (X3Y2) ((X1 AGR) 3-SING) ((X1 DEF
DEF) ((X3 AGR) 3-SING) ((X3 COUNT)
) ((Y1 DEF) DEF) ((Y3 DEF) DEF) ((Y2 AGR)
3-SING) ((Y2 GENDER) (Y4 GENDER)) )

• Value constraints
• Agreement constraints

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Transfer Rules ? Transfer Trees
NP PP NP1 NP P Adj N
N1 ke eka aXyAya N
jIvana
NP NP1 PP Adj N
P NP one chapter of N1
N life
NP1 ke NP2 -gt NP2 of NP1 Ex jIvana ke
eka aXyAya life of (one) chapter
gt a chapter of life NP,12 NPNP PP
NP1 -gt NP1 PP ( (X1Y2) (X2Y1) ((x2
lexwx) 'kA') ) NP,13 NPNP NP1 -gt
NP1 ( (X1Y1) ) PP,12 PPPP NP Postp
-gt Prep NP ( (X1Y2) (X2Y1) )
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Rule Learning - Overview

• Goal Acquire Syntactic Transfer Rules
• Use available knowledge from the source side
  (grammatical structure)
• Three steps
• Flat Seed Generation first guesses at transfer
  rules flat syntactic structure
• Compositionality Learning use previously learned
  rules to learn hierarchical structure
• Constraint Learning refine rules by learning
  appropriate feature constraints

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Flat Seed Rule Generation
Learning Example NP Eng the big apple Heb ha-tapuax ha-gadol
Generated Seed Rule NPNP ART ADJ N ? ART N ART ADJ ((X1Y1) (X1Y3) (X2Y4) (X3Y2))
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Compositionality Learning
Initial Flat Rules SS ART ADJ N V ART N ? ART N ART ADJ V P ART N ((X1Y1) (X1Y3) (X2Y4) (X3Y2) (X4Y5) (X5Y7) (X6Y8)) NPNP ART ADJ N ? ART N ART ADJ ((X1Y1) (X1Y3) (X2Y4) (X3Y2)) NPNP ART N ? ART N ((X1Y1) (X2Y2))
Generated Compositional Rule SS NP V NP ? NP V P NP ((X1Y1) (X2Y2) (X3Y4))
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Constraint Learning
Input Rules and their Example Sets SS NP V NP ? NP V P NP ex1,ex12,ex17,ex26 ((X1Y1) (X2Y2) (X3Y4)) NPNP ART ADJ N ? ART N ART ADJ ex2,ex3,ex13 ((X1Y1) (X1Y3) (X2Y4) (X3Y2)) NPNP ART N ? ART N ex4,ex5,ex6,ex8,ex10,ex11 ((X1Y1) (X2Y2))
Output Rules with Feature Constraints SS NP V NP ? NP V P NP ((X1Y1) (X2Y2) (X3Y4) (X1 NUM X2 NUM) (Y1 NUM Y2 NUM) (X1 NUM Y1 NUM))
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AVENUE Prototypes

• General XFER framework under development for past
  three years
• Prototype systems so far
• German-to-English, Dutch-to-English
• Chinese-to-English
• Hindi-to-English
• Hebrew-to-English
• Portuguese-to-English
• In progress or planned
• Mapudungun-to-Spanish
• Quechua-to-Spanish
• Inupiaq-to-English
• Native-Brazilian languages to Brazilian Portuguese

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Mapudungun

• Indigenous Language of Chile and Argentina
• 1 Million Mapuche Speakers

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Collaboration
Eliseo Cañulef Rosendo Huisca Hugo Carrasco
Hector Painequeo Flor Caniupil Luis Caniupil
Huaiquiñir Marcela Collio Calfunao Cristian
Carrillan Anton Salvador Cañulef

• Mapuche Language Experts
• Universidad de la Frontera (UFRO)
• Instituto de Estudios Indígenas (IEI)
• Institute for Indigenous Studies
• Chilean Funding
• Chilean Ministry of Education (Mineduc)
• Bilingual and Multicultural Education Program

Carolina Huenchullan Arrúe Claudio Millacura
Salas
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Accomplishments

• Corpora Collection
• Spoken Corpus
• Collected Luis Caniupil Huaiquiñir
• Medical Domain
• 3 of 4 Mapudungun Dialects
• 120 hours of Nguluche
• 30 hours of Lafkenche
• 20 hours of Pwenche
• Transcribed in Mapudungun
• Translated into Spanish
• Written Corpus
• 200,000 words
• Bilingual Mapudungun Spanish
• Historical and newspaper text

nmlch-nmjm1_x_0405_nmjm_00 M ltSPAgtno pütokovilu
kay ko C no, si me lo tomaba con agua M
chumgechi pütokoki femuechi pütokon pu ltNoisegt
C como se debe tomar, me lo tomé
pués nmlch-nmjm1_x_0406_nmlch_00 M
Chengewerkelafuymiürke C Ya no estabas como
gente entonces!
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Accomplishments

• Developed At UFRO
• Bilingual Dictionary with Examples
• 1,926 entries
• Spelling Corrected Mapudungun Word List
• 117,003 fully-inflected word forms
• Segmented Word List
• 15,120 forms
• Stems translated into Spanish

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Accomplishments

• Developed at LTI using Mapudungun language
  resources from UFRO
• Spelling Checker
• Integrated into OpenOffice
• Hand-built Morphological Analyzer
• Prototype Machine Translation Systems
• Rule-Based
• Example-Based
• Website LenguasAmerindias.org

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Challenges for Hebrew MT

• Paucity in existing language resources for Hebrew
• No publicly available broad coverage
  morphological analyzer
• No publicly available bilingual lexicons or
  dictionaries
• No POS-tagged corpus or parse tree-bank corpus
  for Hebrew
• No large Hebrew/English parallel corpus
• Scenario well suited for CMU transfer-based MT
  framework for languages with limited resources

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Hebrew-to-English MT Prototype

• Initial prototype developed within a two month
  intensive effort
• Accomplished
• Adapted available morphological analyzer
• Constructed a preliminary translation lexicon
• Translated and aligned Elicitation Corpus
• Learned XFER rules
• Developed (small) manual XFER grammar as a point
  of comparison
• System debugging and development
• Evaluated performance on unseen test data using
  automatic evaluation metrics

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Morphology Example

• Input word BWRH
• 0 1 2 3 4
• --------BWRH--------
• -----B-----WR--H--
• --B---H----WRH---

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Morphology Example

• Y0 ((SPANSTART 0) Y1 ((SPANSTART 0)
  Y2 ((SPANSTART 1)
• (SPANEND 4) (SPANEND
  2) (SPANEND 3)
• (LEX BWRH) (LEX B)
  (LEX WR)
• (POS N) (POS
  PREP)) (POS N)
• (GEN F)
  (GEN M)
• (NUM S)
  (NUM S)
• (STATUS ABSOLUTE))
  (STATUS ABSOLUTE))
• Y3 ((SPANSTART 3) Y4 ((SPANSTART 0)
  Y5 ((SPANSTART 1)
• (SPANEND 4) (SPANEND
  1) (SPANEND 2)
• (LEX LH) (LEX
  B) (LEX H)
• (POS POSS)) (POS
  PREP)) (POS DET))
• Y6 ((SPANSTART 2) Y7 ((SPANSTART 0)
• (SPANEND 4) (SPANEND
  4)
• (LEX WRH) (LEX
  BWRH)
• (POS N) (POS
  LEX))
• (GEN F)
• (NUM S)

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Example Translation

• Input
• ???? ?????? ???? ?????? ?????? ????? ???? ??
  ????? ??????
• After debates many decided the government to hold
  referendum in issue the withdrawal
• Output
• AFTER MANY DEBATES THE GOVERNMENT DECIDED TO HOLD
  A REFERENDUM ON THE ISSUE OF THE WITHDRAWAL

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Sample Output (dev-data)

• maxwell anurpung comes from ghana for israel four
  years ago and since worked in cleaning in hotels
  in eilat
• a few weeks ago announced if management club
  hotel that for him to leave israel according to
  the government instructions and immigration
  police
• in a letter in broken english which spread among
  the foreign workers thanks to them hotel for
  their hard work and announced that will purchase
  for hm flight tickets for their countries from
  their money

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Challenges and Future Directions

• Automatic Transfer Rule Learning
• Learning mappings for non-compositional
  structures
• Effective models for rule scoring for
• Decoding using scores at runtime
• Pruning the large collections of learned rules
• Learning Unification Constraints
• In the absence of morphology or POS annotated
  lexica
• Integrated Xfer Engine and Decoder
• Improved models for scoring tree-to-tree
  mappings, integration with LM and other knowledge
  sources in the course of the search

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Challenges and Future Directions

• Our approach for learning transfer rules is
  applicable to the large parallel data scenario,
  subject to solutions for several big challenges
• No elicitation corpus ? break-down parallel
  sentences into reasonable learning examples
• Working with less reliable automatic word
  alignments rather than manual alignments
• Effective use of reliable parse structures for
  ONE language (i.e. English) and automatic word
  alignments in order to decompose the translation
  of a sentence into several compositional rules.
• Effective scoring of resulting very large
  transfer grammars, and scaled up transfer
  decoding

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Future Research Directions

• Automatic Rule Refinement
• Morphology Learning
• Feature Detection and Corpus Navigation

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Implications for MT with Vast Amounts of Parallel
Data

• Phrase-to-phrase MT ill suited for long-range
  reorderings ? ungrammatical output
• Recent work on hierarchical Stat-MT Chiang,
  2005 and parsing-based MT Melamed et al, 2005
  Knight et al
• Learning general tree-to-tree syntactic mappings
  is equally problematic
• Meaning is a hybrid of complex, non-compositional
  phrases embedded within a syntactic structure
• Some constituents can be translated in isolation,
  others require contextual mappings

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Evaluation Results

• Test set of 62 sentences from Haaretz newspaper,
  2 reference translations

System BLEU NIST P R METEOR
No Gram 0.0616 3.4109 0.4090 0.4427 0.3298
Learned 0.0774 3.5451 0.4189 0.4488 0.3478
Manual 0.1026 3.7789 0.4334 0.4474 0.3617
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Hebrew-English Test Suite Evaluation
Grammar BLEU METEOR
Baseline (NoGram) 0.0996 0.4916
Learned Grammar 0.1608 0.5525
Manual Grammar 0.1642 0.5320
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Quechua?Spanish MT

• V-Unit funded Summer project in Cusco (Peru)
  June-August 2005 preparations and data
  collection started earlier
• Intensive Quechua course in Centro Bartolome de
  las Casas (CBC)
• Worked together with two Quechua native and one
  non-native speakers on developing infrastructure
  (correcting elicited translations, segmenting and
  translating list of most frequent words)

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Quechua ? Spanish Prototype MT System

• Stem Lexicon (semi-automatically generated) 753
  lexical entries
• Suffix lexicon 21 suffixes
• (150 Cusihuaman)
• Quechua morphology analyzer
• 25 translation rules
• Spanish morphology generation module
• User-Studies 10 sentences, 3 users (2 native, 1
  non-native)

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The Transfer Engine
Analysis Source text is parsed into its grammatical structure. Determines transfer application ordering. Example ? ? ??(he read book) S NP VP N V NP ? ? ? Transfer A target language tree is created by reordering, insertion, and deletion. S NP VP N V NP he read DET N a book Article a is inserted into object NP. Source words translated with transfer lexicon. Generation Target language constraints are checked and final translation produced. E.g. reads is chosen over read to agree with he. Final translation He reads a book
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The Transfer Engine

• Some Unique Features
• Works with either learned or manually-developed
  transfer grammars
• Handles rules with or without unification
  constraints
• Supports interfacing with servers for
  Morphological analysis and generation
• Can handle ambiguous source-word analyses and/or
  SL segmentations represented in the form of
  lattice structures

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The Lattice Decoder

• Simple Stack Decoder, similar in principle to
  SMT/EBMT decoders
• Searches for best-scoring path of non-overlapping
  lattice arcs
• Scoring based on log-linear combination of
  scoring components (no MER training yet)
• Scoring components
• Standard trigram LM
• Fragmentation how many arcs to cover the entire
  translation?
• Length Penalty
• Rule Scores (not fully integrated yet)

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Outline

• Rationale for learning-based MT
• Roadmap for learning-based MT
• Framework overview
• Elicitation
• Learning transfer Rules
• Automatic rule refinement
• Example prototypes
• Implications for MT with vast parallel data
• Conclusions and future directions

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Data Elicitation for Languages with Limited
Resources

• Rationale
• Large volumes of parallel text not available ?
  create a small maximally-diverse parallel corpus
  that directly supports the learning task
• Bilingual native informant(s) can translate and
  align a small pre-designed elicitation corpus,
  using elicitation tool
• Elicitation corpus designed to be typologically
  and structurally comprehensive and compositional
• Transfer-rule engine and new learning approach
  support acquisition of generalized transfer-rules
  from the data

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Elicitation Tool English-Chinese Example
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Elicitation ToolEnglish-Chinese Example
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Elicitation ToolEnglish-Hindi Example
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Elicitation ToolEnglish-Arabic Example
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Elicitation ToolSpanish-Mapudungun Example
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Designing Elicitation Corpora

• What do we want to elicit?
• Diversity of linguistic phenomena and
  constructions
• Syntactic structural diversity
• How do we construct an elicitation corpus?
• Typological Elicitation Corpus based on
  elicitation and documentation work of field
  linguists (e.g. Comrie 1977, Bouquiaux 1992)
  initial corpus size 1000 examples
• Structural Elicitation Corpus based on
  representative sample of English phrase
  structures 120 examples
• Organized compositionally elicit simple
  structures first, then use them as building
  blocks
• Goal minimize size, maximize linguistic coverage
  

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Typological Elicitation Corpus

• Feature Detection
• Discover what features exist in the language and
  where/how they are marked
• Example does the language mark gender of nouns?
  How and where are these marked?
• Method compare translations of minimal pairs
  sentences that differ in only ONE feature
• Elicit translations/alignments for detected
  features and their combinations
• Dynamic corpus navigation based on feature
  detection no need to elicit for combinations
  involving non-existent features

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Typological Elicitation Corpus

• Initial typological corpus of about 1000
  sentences was manually constructed
• New construction methodology for building an
  elicitation corpus using
• A feature specification lists inventory of
  available features and their values
• A definition of the set of desired feature
  structures
• Schemas define sets of desired combinations of
  features and values
• Multiplier algorithm generates the comprehensive
  set of feature structures
• A generation grammar and lexicon NLG generator
  generates NL sentences from the feature structures

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Structural Elicitation Corpus

• Goal create a compact diverse sample corpus of
  syntactic phrase structures in English in order
  to elicit how these map into the elicited
  language
• Methodology
• Extracted all CFG rules from Brown section of
  Penn TreeBank (122K sentences)
• Simplified POS tag set
• Constructed frequency histogram of extracted
  rules
• Pulled out simplest phrases for most frequent
  rules for NPs, PPs, ADJPs, ADVPs, SBARs and
  Sentences
• Some manual inspection and refinement
• Resulting corpus of about 120 phrases/sentences
  representing common structures
• See Probst and Lavie, 2004

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Outline

• Rationale for learning-based MT
• Roadmap for learning-based MT
• Framework overview
• Elicitation
• Learning transfer Rules
• Automatic rule refinement
• Example prototypes
• Implications for MT with vast parallel data
• Conclusions and future directions

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Flat Seed Rule Generation

• Create a flat transfer rule specific to the
  sentence pair, partially abstracted to POS
• Words that are aligned word-to-word and have the
  same POS in both languages are generalized to
  their POS
• Words that have complex alignments (or not the
  same POS) remain lexicalized
• One seed rule for each translation example
• No feature constraints associated with seed rules
  (but mark the example(s) from which it was
  learned)

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Compositionality Learning

• Detection traverse the c-structure of the
  English sentence, add compositional structure for
  translatable chunks
• Generalization adjust constituent sequences and
  alignments
• Two implemented variants
• Safe Compositionality there exists a transfer
  rule that correctly translates the
  sub-constituent
• Maximal Compositionality Generalize the rule if
  supported by the alignments, even in the absence
  of an existing transfer rule for the
  sub-constituent

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Constraint Learning

• Goal add appropriate feature constraints to the
  acquired rules
• Methodology
• Preserve general structural transfer
• Learn specific feature constraints from example
  set
• Seed rules are grouped into clusters of similar
  transfer structure (type, constituent sequences,
  alignments)
• Each cluster forms a version space a partially
  ordered hypothesis space with a specific and a
  general boundary
• The seed rules in a group form the specific
  boundary of a version space
• The general boundary is the (implicit) transfer
  rule with the same type, constituent sequences,
  and alignments, but no feature constraints

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Constraint Learning Generalization

• The partial order of the version space
• Definition A transfer rule tr1 is strictly more
  general than another transfer rule tr2 if all
  f-structures that are satisfied by tr2 are also
  satisfied by tr1.
• Generalize rules by merging them
• Deletion of constraint
• Raising two value constraints to an agreement
  constraint, e.g.
• ((x1 num) pl), ((x3 num) pl) ?
• ((x1 num) (x3 num))

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Automated Rule Refinement

• Bilingual informants can identify translation
  errors and pinpoint the errors
• A sophisticated trace of the translation path can
  identify likely sources for the error and do
  Blame Assignment
• Rule Refinement operators can be developed to
  modify the underlying translation grammar (and
  lexicon) based on characteristics of the error
  source
• Add or delete feature constraints from a rule
• Bifurcate a rule into two rules (general and
  specific)
• Add or correct lexical entries
• See Font-Llitjos, Carbonell Lavie, 2005

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Outline

• Rationale for learning-based MT
• Roadmap for learning-based MT
• Framework overview
• Elicitation
• Learning transfer Rules
• Automatic rule refinement
• Example prototypes
• Implications for MT with vast parallel data
• Conclusions and future directions

56
Outline

• Rationale for learning-based MT
• Roadmap for learning-based MT
• Framework overview
• Elicitation
• Learning transfer Rules
• Automatic rule refinement
• Learning Morphology
• Example prototypes
• Implications for MT with vast parallel data
• Conclusions and future directions

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Implications for MT with Vast Amounts of Parallel
Data

• Example
• ? ?? ? ??? ?? ? ??
• He freq with J Zemin Pres via
  phone
• He freq talked with President J Zemin over
  the phone

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Implications for MT with Vast Amounts of Parallel
Data

• Example
• ? ?? ? ??? ?? ? ??
• He freq with J Zemin Pres via
  phone
• He freq talked with President J Zemin over
  the phone

NP1
NP2
NP3
NP1
NP2
NP3
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Conclusions

• There is hope yet for wide-spread MT between many
  of the worlds language pairs
• MT offers a fertile yet extremely challenging
  ground for learning-based approaches that
  leverage from diverse sources of information
• Syntactic structure of one or both languages
• Word-to-word correspondences
• Decomposable units of translation
• Statistical Language Models
• AVENUEs XFER approach provides a feasible
  solution to MT for languages with limited
  resources
• Promising approach for addressing the fundamental
  weaknesses in current corpus-based MT for
  languages with vast resources

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61
Mapudungun-to-Spanish Example
English I didnt see Maria
Mapudungun pelafiñ Maria
Spanish No vi a María
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Mapudungun-to-Spanish Example
English I didnt see Maria
Mapudungun pelafiñ Maria pe -la -fi -ñ Maria see
-neg -3.obj -1.subj.indicative Maria
Spanish No vi a María No vi a María neg see.1.sub
j.past.indicative acc Maria
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pe-la-fi-ñ Maria
V
pe
64
pe-la-fi-ñ Maria
V
pe
VSuff
Negation
la
65
pe-la-fi-ñ Maria
V
pe
VSuffG
Pass all features up
VSuff
la
66
pe-la-fi-ñ Maria
V
pe
VSuffG
VSuff
object person 3
fi
VSuff
la
67
pe-la-fi-ñ Maria
V
VSuffG
pe
Pass all features up from both children
VSuffG
VSuff
fi
VSuff
la
68
pe-la-fi-ñ Maria
V
VSuffG
VSuff
pe
person 1 number sg mood ind
VSuffG
VSuff
ñ
fi
VSuff
la
69
pe-la-fi-ñ Maria
V
VSuffG
VSuffG
VSuff
pe
Pass all features up from both children
VSuffG
VSuff
ñ
fi
VSuff
la
70
pe-la-fi-ñ Maria
Pass all features up from both children
V
Check that 1) negation 2) tense is undefined
V
VSuffG
VSuffG
VSuff
pe
VSuffG
VSuff
ñ
fi
VSuff
la
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pe-la-fi-ñ Maria
NP
V
VSuffG
person 3 number sg human
VSuffG
VSuff
N
pe
VSuffG
VSuff
Maria
ñ
fi
VSuff
la
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pe-la-fi-ñ Maria
S
Check that NP is human
Pass features up from
VP
NP
V
VSuffG
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
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Transfer to Spanish Top-Down
S
S
VP
VP
NP
V
VSuffG
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
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Transfer to Spanish Top-Down
Pass all features to Spanish side
S
S
VP
VP
NP
NP
a
V
VSuffG
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
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Transfer to Spanish Top-Down
S
S
Pass all features down
VP
VP
NP
NP
a
V
VSuffG
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
76
Transfer to Spanish Top-Down
S
S
Pass object features down
VP
VP
NP
NP
a
V
VSuffG
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
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Transfer to Spanish Top-Down
S
S
VP
VP
NP
NP
a
V
VSuffG
Accusative marker on objects is introduced
because human
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
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Transfer to Spanish Top-Down
S
S
VP
VP
VPVP VBar NP -gt VBar "a" NP ( (X1Y1) (X2
Y3) ((X2 type) (NOT personal)) ((X2
human) c ) (X0 X1) ((X0 object) X2)
(Y0 X0) ((Y0 object) (X0 object)) (Y1
Y0) (Y3 (Y0 object)) ((Y1 objmarker person)
(Y3 person)) ((Y1 objmarker number) (Y3
number)) ((Y1 objmarker gender) (Y3 ender)))
NP
NP
a
V
VSuffG
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
79
Transfer to Spanish Top-Down
S
S
Pass person, number, and mood features to Spanish
Verb
VP
VP
NP
NP
a
Assign tense past
V
VSuffG
V
no
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
80
Transfer to Spanish Top-Down
S
S
VP
VP
NP
NP
a
V
VSuffG
V
no
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
Introduced because negation
fi
VSuff
la
81
Transfer to Spanish Top-Down
S
S
VP
VP
NP
NP
a
V
VSuffG
V
no
VSuffG
VSuff
N
pe
ver
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
82
Transfer to Spanish Top-Down
S
S
VP
VP
NP
NP
a
V
VSuffG
V
no
VSuffG
VSuff
N
pe
ver
vi
VSuffG
VSuff
ñ
Maria
person 1 number sg mood indicative tense
past
fi
VSuff
la
83
Transfer to Spanish Top-Down
S
S
Pass features over to Spanish side
VP
VP
NP
NP
a
V
VSuffG
V
no
VSuffG
VSuff
N
pe
vi
N
VSuffG
VSuff
ñ
Maria
María
fi
VSuff
la
84
I Didnt see Maria
S
S
VP
VP
NP
NP
a
V
VSuffG
V
no
VSuffG
VSuff
N
pe
vi
N
VSuffG
VSuff
ñ
Maria
María
fi
VSuff
la
85
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