AVENUE: Learningbased MT Approaches for Languages with Limited Resources

1
AVENUELearning-based MT Approaches for
Languages with Limited Resources

• Alon Lavie
• Language Technologies Institute
• Carnegie Mellon University
• Joint work with
• Jaime Carbonell, Lori Levin, Kathrin Probst, Erik
  Peterson, Christian Monson, Ariadna Font-Llitjos,
  Alison Alvarez, Roberto Aranovich

2
Machine Translation Where are we today?

• Age of Internet and Globalization great demand
  for MT
• Multiple official languages of UN, EU, Canada,
  etc.
• Documentation dissemination for large
  manufacturers (Microsoft, IBM, Caterpillar)
• Economic incentive is still primarily within a
  small number of language pairs
• Some fairly good commercial products in the
  market for these language pairs
• Primarily a product of rule-based systems after
  many years of development
• Pervasive MT between most language pairs still
  non-existent and not on the immediate horizon

3
Progression of MT

• Started with rule-based systems
• Very large expert human effort to construct
  language-specific resources (grammars, lexicons)
• High-quality MT extremely expensive ? only for
  handful of language pairs
• Along came EBMT and then SMT
• Replaced human effort with extremely large
  volumes of parallel text data
• Less expensive, but still only feasible for a
  small number of language pairs
• We traded human labor with data
• Where does this take us in 5-10 years?
• Large parallel corpora for maybe 25-50 language
  pairs
• What about all the other languages?
• Is all this data (with very shallow
  representation of language structure) really
  necessary?
• Can we build MT approaches that learn deeper
  levels of language structure and how they map
  from one language to another?

4
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

5
The Roadmap to Learning-based MT

• Automatic acquisition of necessary language
  resources and knowledge using machine learning
  methodologies
• Learning morphology (analysis/generation)
• Rapid acquisition of broad coverage word-to-word
  and phrase-to-phrase translation lexicons
• Learning of syntactic structural mappings
• Tree-to-tree structure transformations Knight et
  al, Eisner, Melamed require parse trees for
  both languages
• Learning syntactic transfer rules with resources
  (grammar, parses) for just one of the two
  languages
• Automatic rule refinement and/or post-editing
• A framework for integrating the acquired MT
  resources into effective MT prototype systems
• Effective integration of acquired knowledge with
  statistical/distributional information

6
CMUs AVENUE Approach

• Elicitation use bilingual native informants to
  produce 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

7
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
8
AVENUE Architecture
9
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

10
Elicitation Tool English-Chinese Example
11
Elicitation ToolEnglish-Chinese Example
12
Elicitation ToolEnglish-Hindi Example
13
Elicitation ToolEnglish-Arabic Example
14
Elicitation ToolSpanish-Mapudungun Example
15
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
  

16
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))

17
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

18
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

19
Flat Seed Rule Generation
20
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)

21
Compositionality Learning
22
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

23
Constraint Learning
24
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

25
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))

26
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

27
Morphology Learning

• Goal Unsupervised learning of morphemes and
  their function from raw monolingual data
• Segmentation of words into morphemes
• Identification of morphological paradigms
  (inflections and derivations)
• Learning association between morphemes and their
  function in the language
• Organize the raw data in the form of a network of
  paradigm candidate schemes
• Search the network for a collection of schemes
  that represent true morphology paradigms of the
  language
• Learn mappings between the schemes and
  features/functions using minimal pairs of
  elicited data
• Construct analyzer based on the collection of
  schemes and the acquired function mappings

28
Example Vocabulary blame blamed blames
roamed roaming roams solve
solves solving
Ø.s.d blame
Ø.s blame solve
e.es blam solv
me.mes bla
s blame roam solve
29
Ø.s.d blame
e.es.ed blam
me.mes.med bla
e.es blam solv
Ø.s blame solve
me.mes bla
e.ed blam
Ø.d blame
me.med bla
s.d blame
es.ed blam
mes.med bla
Ø blame blames blamed roams roamed roaming solve s
olves solving
e blam solv
me bla
s blame roam solve
es blam solv
mes bla
ed blam roam
d blame roame
med bla roa
29
30
a.as.o.os.tro 1 cas

• Spanish Newswire Corpus
• 40,011 Tokens
• 6,975 Types

a.as.o.os 43 african, cas, jurídic, l, ...
a.as.os 50 afectad, cas, jurídic, l, ...
a.as.o 59 cas, citad, jurídic, l, ...
a.o.os 105 impuest, indonesi, italian, jurídic,
...
as.o.os 54 cas, implicad, jurídic, l, ...
a.as 199 huelg, incluid, industri, inundad, ...
a.os 134 impedid, impuest, indonesi, inundad, ...
as.os 68 cas, implicad, inundad, jurídic, ...
a.o 214 id, indi, indonesi, inmediat, ...
as.o 85 intern, jurídic, just, l, ...
a.tro 2 cas.cen
o.os 268 human, implicad, indici,
indocumentad, ...
a 1237 huelg, ib, id, iglesi, ...
as 404 huelg, huelguist, incluid, industri, ...
os 534 humorístic, human, hígad, impedid, ...
o 1139 hub, hug, human, huyend, ...
tro 16 catas, ce, cen, cua, ...
30
31
a.as.o.os.tro 1 cas
C-Suffixes C-Stems
Level 5 5 C-suffixes C-Stem Type Count
a.as.o.os 43 african, cas, jurídic, l, ...
a.as.os 50 afectad, cas, jurídic, l, ...
a.as.o 59 cas, citad, jurídic, l, ...
a.o.os 105 impuest, indonesi, italian, jurídic,
...
as.o.os 54 cas, implicad, jurídic, l, ...
a.as 199 huelg, incluid, industri, inundad, ...
a.os 134 impedid, impuest, indonesi, inundad, ...
as.os 68 cas, implicad, inundad, jurídic, ...
a.o 214 id, indi, indonesi, inmediat, ...
as.o 85 intern, jurídic, just, l, ...
a.tro 2 cas.cen
o.os 268 human, implicad, indici,
indocumentad, ...
a 1237 huelg, ib, id, iglesi, ...
as 404 huelg, huelguist, incluid, industri, ...
os 534 humorístic, human, hígad, impedid, ...
o 1139 hub, hug, human, huyend, ...
tro 16 catas, ce, cen, cua, ...
31
32
Adjective Inflection Class
From the spurious c-suffix tro
a.as.o.os 43 african, cas, jurídic, l, ...
a.as.os 50 afectad, cas, jurídic, l, ...
a.as.o 59 cas, citad, jurídic, l, ...
a.o.os 105 impuest, indonesi, italian, jurídic,
...
as.o.os 54 cas, implicad, jurídic, l, ...
a.as 199 huelg, incluid, industri, inundad, ...
a.os 134 impedid, impuest, indonesi, inundad, ...
as.os 68 cas, implicad, inundad, jurídic, ...
a.o 214 id, indi, indonesi, inmediat, ...
as.o 85 intern, jurídic, just, l, ...
o.os 268 human, implicad, indici,
indocumentad, ...
a 1237 huelg, ib, id, iglesi, ...
as 404 huelg, huelguist, incluid, industri, ...
os 534 humorístic, human, hígad, impedid, ...
o 1139 hub, hug, human, huyend, ...
32
33
Basic Search Procedure
a.as.o.os 43 african, cas, jurídic, l, ...
a.as.os 50 afectad, cas, jurídic, l, ...
a.as.o 59 cas, citad, jurídic, l, ...
a.o.os 105 impuest, indonesi, italian, jurídic,
...
as.o.os 54 cas, implicad, jurídic, l, ...
a.as 199 huelg, incluid, industri, inundad, ...
a.os 134 impedid, impuest, indonesi, inundad, ...
as.os 68 cas, implicad, inundad, jurídic, ...
a.o 214 id, indi, indonesi, inmediat, ...
as.o 85 intern, jurídic, just, l, ...
o.os 268 human, implicad, indici,
indocumentad, ...
a 1237 huelg, ib, id, iglesi, ...
as 404 huelg, huelguist, incluid, industri, ...
os 534 humorístic, human, hígad, impedid, ...
o 1139 hub, hug, human, huyend, ...
33
34
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
• In progress or planned
• Mapudungun-to-Spanish
• Quechua-to-Spanish
• Arabic-to-English
• Native-Brazilian languages to Brazilian Portuguese

35
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

36
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

37
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38
Morphology Example

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

39
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)

40
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

41
Evaluation Results

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

42
Hebrew-English Test Suite Evaluation
43
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
• Provides a feasible solution to MT for languages
  with limited resources
• Extremely promising approach for addressing the
  fundamental weaknesses in current corpus-based MT
  for languages with vast resources

44
Future Research Directions

• Automatic Transfer Rule Learning
• In the large-data scenario from large volumes
  of uncontrolled parallel text automatically
  word-aligned
• In the absence of morphology or POS annotated
  lexica
• 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
• 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

45
Future Research Directions

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

46
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47
Mapudungun-to-Spanish Example
English I didnt see Maria
Mapudungun pelafiñ Maria
Spanish No vi a María
48
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
49
pe-la-fi-ñ Maria
V
pe
50
pe-la-fi-ñ Maria
V
pe
VSuff
Negation
la
51
pe-la-fi-ñ Maria
V
pe
VSuffG
Pass all features up
VSuff
la
52
pe-la-fi-ñ Maria
V
pe
VSuffG
VSuff
object person 3
fi
VSuff
la
53
pe-la-fi-ñ Maria
V
VSuffG
pe
Pass all features up from both children
VSuffG
VSuff
fi
VSuff
la
54
pe-la-fi-ñ Maria
V
VSuffG
VSuff
pe
person 1 number sg mood ind
VSuffG
VSuff
ñ
fi
VSuff
la
55
pe-la-fi-ñ Maria
V
VSuffG
VSuffG
VSuff
pe
Pass all features up from both children
VSuffG
VSuff
ñ
fi
VSuff
la
56
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
57
pe-la-fi-ñ Maria
NP
V
VSuffG
person 3 number sg human
VSuffG
VSuff
N
pe
VSuffG
VSuff
Maria
ñ
fi
VSuff
la
58
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
59
Transfer to Spanish Top-Down
S
S
VP
VP
NP
V
VSuffG
VSuffG
VSuff
N
pe
VSuffG
VSuff
ñ
Maria
fi
VSuff
la
60
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
61
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
62
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
63
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
64
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
65
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
66
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
67
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
68
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
69
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
70
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
71
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