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Learning-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. - PowerPoint PPT Presentation
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Nov 17, 2005 Learning-based MT 1
Learning-based MT Approaches for Languages with Limited
Resources
Alon LavieLanguage 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
Nov 17, 2005 Learning-based MT 2
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
Nov 17, 2005 Learning-based MT 3
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
Nov 17, 2005 Learning-based MT 4
Mi chiamo Alon Lavie My name is Alon Lavie
Give-information+personal-data (name=alon_lavie)
[s [vp accusative_pronoun “chiamare” proper_name]]
[s [np [possessive_pronoun “name”]]
[vp “be” proper_name]]
Direct
Transfer
Interlingua
Analysis Generation
Approaches to MT: Vaquois MT Triangle
Nov 17, 2005 Learning-based MT 5
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?
Nov 17, 2005 Learning-based MT 6
Why Machine Translation for Languages with Limited Resources?
• We are in the age of information explosion– The internet+web+Google 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
Nov 17, 2005 Learning-based MT 7
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
Nov 17, 2005 Learning-based MT 8
CMU’s 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
Nov 17, 2005 Learning-based MT 9
AVENUE Architecture
Learning Module
Transfer Rules
{PP,4894};;Score:0.0470PP::PP [NP POSTP] -> [PREP NP]((X2::Y1)(X1::Y2))
Translation Lexicon
Run Time Transfer System
Lattice Decoder
English Language Model
Word-to-Word Translation Probabilities
Word-aligned elicited data
Nov 17, 2005 Learning-based MT 10
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
Nov 17, 2005 Learning-based MT 11
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
Nov 17, 2005 Learning-based MT 17
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
Nov 17, 2005 Learning-based MT 18
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
Nov 17, 2005 Learning-based MT 19
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
Nov 17, 2005 Learning-based MT 20
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]
Nov 17, 2005 Learning-based MT 21
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
Nov 17, 2005 Learning-based MT 22
Transfer Rule Formalism
Type informationPart-of-speech/constituent
informationAlignments
x-side constraints
y-side constraints
xy-constraints, e.g. ((Y1 AGR) = (X1 AGR))
;SL: the old man, TL: ha-ish ha-zaqen
NP::NP [DET ADJ N] -> [DET N DET ADJ]((X1::Y1)(X1::Y3)(X2::Y4)(X3::Y2)
((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)))
Nov 17, 2005 Learning-based MT 23
Transfer Rule Formalism (II)
Value constraints
Agreement constraints
;SL: the old man, TL: ha-ish ha-zaqen
NP::NP [DET ADJ N] -> [DET N DET ADJ]((X1::Y1)(X1::Y3)(X2::Y4)(X3::Y2)
((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)))
Nov 17, 2005 Learning-based MT 24
Rule Learning - Overview
• Goal: Acquire Syntactic Transfer Rules• Use available knowledge from the source
side (grammatical structure)• Three steps:
1. Flat Seed Generation: first guesses at transfer rules; flat syntactic structure
2. Compositionality Learning: use previously learned rules to learn hierarchical structure
3. Constraint Learning: refine rules by learning appropriate feature constraints
Nov 17, 2005 Learning-based MT 25
Flat Seed Rule Generation
Learning Example: NP
Eng: the big apple
Heb: ha-tapuax ha-gadol
Generated Seed Rule:
NP::NP [ART ADJ N] [ART N ART ADJ]
((X1::Y1)
(X1::Y3)
(X2::Y4)
(X3::Y2))
Nov 17, 2005 Learning-based MT 26
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)
Nov 17, 2005 Learning-based MT 27
Compositionality LearningInitial Flat Rules: S::S [ART ADJ N V ART N] [ART N ART ADJ V P ART N]
((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2) (X4::Y5) (X5::Y7) (X6::Y8))
NP::NP [ART ADJ N] [ART N ART ADJ]
((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2))
NP::NP [ART N] [ART N]
((X1::Y1) (X2::Y2))
Generated Compositional Rule:
S::S [NP V NP] [NP V P NP]
((X1::Y1) (X2::Y2) (X3::Y4))
Nov 17, 2005 Learning-based MT 28
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
Nov 17, 2005 Learning-based MT 29
Constraint LearningInput: Rules and their Example Sets
S::S [NP V NP] [NP V P NP] {ex1,ex12,ex17,ex26}
((X1::Y1) (X2::Y2) (X3::Y4))
NP::NP [ART ADJ N] [ART N ART ADJ] {ex2,ex3,ex13}
((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2))
NP::NP [ART N] [ART N] {ex4,ex5,ex6,ex8,ex10,ex11}
((X1::Y1) (X2::Y2))
Output: Rules with Feature Constraints:
S::S [NP V NP] [NP V P NP]
((X1::Y1) (X2::Y2) (X3::Y4)
(X1 NUM = X2 NUM)
(Y1 NUM = Y2 NUM)
(X1 NUM = Y1 NUM))
Nov 17, 2005 Learning-based MT 30
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
Nov 17, 2005 Learning-based MT 31
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))
Nov 17, 2005 Learning-based MT 32
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]
Nov 17, 2005 Learning-based MT 33
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
Nov 17, 2005 Learning-based MT 34
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
Nov 17, 2005 Learning-based MT 35
Ø.sblamesolve
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Ø.s.dblame
sblameroamsolve
e.esblamsolv
me.mesbla
e.esblamsolv
e.edblam
esblamsolv
Ø.s.dblame
Ø.sblamesolve
Øblameblamesblamedroams
roamedroaming
solvesolvessolving
e.es.edblam
edblamroam
dblameroame
Ø.dblame
s.dblame
sblameroamsolve
es.edblam
eblamsolv
me.mesbla
me.medbla
mesbla
me.mes.medbla
medblaroa
mes.medbla
mebla
36
a.as.o.os43
african, cas, jurídic, l, ...
a.as.o.os.tro1
cas
a.as.os50
afectad, cas, jurídic, l, ...
a.as.o59
cas, citad, jurídic, l, ...
a.o.os105
impuest, indonesi, italian, jurídic, ...
a.as199
huelg, incluid, industri,
inundad, ...
a.os134
impedid, impuest, indonesi,
inundad, ...
as.os68
cas, implicad, inundad, jurídic, ...
a.o214
id, indi, indonesi,
inmediat, ...
as.o85
intern, jurídic, just, l, ...
a.tro2
cas.cen
a1237
huelg, ib, id, iglesi, ...
as404
huelg, huelguist, incluid,
industri, ...
os534
humorístic, human, hígad,
impedid, ...
o1139
hub, hug, human,
huyend, ...
tro16
catas, ce, cen, cua, ...
as.o.os54
cas, implicad, jurídic, l, ...
o.os268
human, implicad, indici,
indocumentad, ...
Spanish Newswire Corpus
40,011 Tokens
6,975 Types
37
a.as.o.os43
african, cas, jurídic, l, ...
a.as.o.os.tro1
cas
a.as.os50
afectad, cas, jurídic, l, ...
a.as.o59
cas, citad, jurídic, l, ...
a.o.os105
impuest, indonesi, italian, jurídic, ...
a.as199
huelg, incluid, industri,
inundad, ...
a.os134
impedid, impuest, indonesi,
inundad, ...
as.os68
cas, implicad, inundad, jurídic, ...
a.o214
id, indi, indonesi,
inmediat, ...
as.o85
intern, jurídic, just, l, ...
a.tro2
cas.cen
a1237
huelg, ib, id, iglesi, ...
as404
huelg, huelguist, incluid,
industri, ...
os534
humorístic, human, hígad,
impedid, ...
o1139
hub, hug, human,
huyend, ...
tro16
catas, ce, cen, cua, ...
as.o.os54
cas, implicad, jurídic, l, ...
o.os268
human, implicad, indici,
indocumentad, ...
C-Suffixes
C-Stems
Level 5 = 5 C-suffixes
C-Stem Type Count
38
a.as.o.os43
african, cas, jurídic, l, ...
a.as.o.os.tro1
cas
a.tro2
cas.cen
tro16
catas, ce, cen, cua, ...
Adjective Inflection Class
39
a.as.os50
afectad, cas, jurídic, l, ...
a.as.o59
cas, citad, jurídic, l, ...
a.o.os105
impuest, indonesi, italian, jurídic, ...
a.as199
huelg, incluid, industri,
inundad, ...
a.os134
impedid, impuest, indonesi,
inundad, ...
as.os68
cas, implicad, inundad, jurídic, ...
a.o214
id, indi, indonesi,
inmediat, ...
as.o85
intern, jurídic, just, l, ...
a1237
huelg, ib, id, iglesi, ...
as404
huelg, huelguist, incluid,
industri, ...
os534
humorístic, human, hígad,
impedid, ...
o1139
hub, hug, human,
huyend, ...
as.o.os54
cas, implicad, jurídic, l, ...
o.os268
human, implicad, indici,
indocumentad, ...
From the spurious c-suffix “tro”
a.as.o.os.tro1
cas
a.tro2
cas.cen
tro16
catas, ce, cen, cua, ...
a.as.o.os43
african, cas, jurídic, l, ...
a.as.os50
afectad, cas, jurídic, l, ...
a.as.o59
cas, citad, jurídic, l, ...
a.o.os105
impuest, indonesi, italian, jurídic, ...
a.as199
huelg, incluid, industri,
inundad, ...
a.os134
impedid, impuest, indonesi,
inundad, ...
as.os68
cas, implicad, inundad, jurídic, ...
a.o214
id, indi, indonesi,
inmediat, ...
as.o85
intern, jurídic, just, l, ...
a1237
huelg, ib, id, iglesi, ...
as404
huelg, huelguist, incluid,
industri, ...
os534
humorístic, human, hígad,
impedid, ...
o1139
hub, hug, human,
huyend, ...
as.o.os54
cas, implicad, jurídic, l, ...
o.os268
human, implicad, indici,
indocumentad, ...
40
De
cre
asin
g C
-Ste
m C
oun
t
Incr
ea
sin
g C
-Su
ffix
Co
unt
Basic Search Procedure
Nov 17, 2005 Learning-based MT 41
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
Nov 17, 2005 Learning-based MT 42
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
Nov 17, 2005 Learning-based MT 43
Challenges for Hebrew MT
• Puacity 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
Nov 17, 2005 Learning-based MT 44
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
Nov 17, 2005 Learning-based MT 45
Morphology Example
• Input word: B$WRH
0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---|
Nov 17, 2005 Learning-based MT 46
Morphology ExampleY0: ((SPANSTART 0) Y1: ((SPANSTART 0) Y2: ((SPANSTART 1) (SPANEND 4) (SPANEND 2) (SPANEND 3) (LEX B$WRH) (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 $WRH) (LEX B$WRH) (POS N) (POS LEX)) (GEN F) (NUM S) (STATUS ABSOLUTE))
Nov 17, 2005 Learning-based MT 47
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
Nov 17, 2005 Learning-based MT 48
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
Nov 17, 2005 Learning-based MT 49
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
Nov 17, 2005 Learning-based MT 50
Implications for MT with Vast Amounts of Parallel Data
• Learning word/short-phrase translations vs. learning long phrase-to-phrase translations
• 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]
• 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
Nov 17, 2005 Learning-based MT 51
Implications for MT with Vast Amounts of Parallel Data
• Our approach for learning transfer rules is applicable to the large data scenario, subject to solutions for several 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
Nov 17, 2005 Learning-based MT 52
Implications for MT with Vast Amounts of Parallel Data
• Example: 他 经常 与 江泽民 总统 通 电话 He freq with J Zemin Pres via phone
He freq talked with President J Zemin over the phone
Nov 17, 2005 Learning-based MT 53
Implications for MT with Vast Amounts of Parallel Data
• Example: 他 经常 与 江泽民 总统 通 电话 He freq with J Zemin Pres via phone
He freq talked with President J Zemin over the phone
NP1
NP1
NP2
NP2
NP3
NP3
Nov 17, 2005 Learning-based MT 54
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
Nov 17, 2005 Learning-based MT 55
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
Nov 17, 2005 Learning-based MT 56
Future Research Directions
• Automatic Rule Refinement• Morphology Learning• Feature Detection and Corpus
Navigation• …
Nov 17, 2005 Learning-based MT 58
Mapudungun-to-Spanish Example
Mapudungun
pelafiñ Maria
Spanish
No vi a María
English
I didn’t see Maria
Nov 17, 2005 Learning-based MT 59
Mapudungun-to-Spanish Example
Mapudungun
pelafiñ Mariape -la -fi -ñ Mariasee -neg -3.obj -1.subj.indicative Maria
Spanish
No vi a MaríaNo vi a Maríaneg see.1.subj.past.indicative acc Maria
English
I didn’t see Maria
Nov 17, 2005 Learning-based MT 64
V
pe
pe-la-fi-ñ Maria
VSuff
la
VSuffG VSuff
fi
VSuffGPass all features up from both children
Nov 17, 2005 Learning-based MT 65
V
pe
pe-la-fi-ñ Maria
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
person = 1number = sgmood = ind
Nov 17, 2005 Learning-based MT 66
V
pe
pe-la-fi-ñ Maria
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
Pass all features up from both children
VSuffG
Nov 17, 2005 Learning-based MT 67
V
V
pe
pe-la-fi-ñ Maria
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
Pass all features up from both children
VSuffGCheck that:1) negation = +2) tense is undefined
Nov 17, 2005 Learning-based MT 68
V
pe
pe-la-fi-ñ Maria
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
V NP
N
Maria
N person = 3number = sghuman = +
Nov 17, 2005 Learning-based MT 69
Pass features up from
V
pe
pe-la-fi-ñ Maria
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
Check that NP is human = +V VP
Nov 17, 2005 Learning-based MT 70
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
Nov 17, 2005 Learning-based MT 71
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
Pass all features to Spanish side
Nov 17, 2005 Learning-based MT 72
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
Pass all features down
Nov 17, 2005 Learning-based MT 73
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
Pass object features down
Nov 17, 2005 Learning-based MT 74
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
Accusative marker on objects is introduced because human = +
Nov 17, 2005 Learning-based MT 75
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
VP::VP [VBar NP] -> [VBar "a" NP]( (X1::Y1)
(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)))
Nov 17, 2005 Learning-based MT 76
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
V“no”
Pass person, number, and mood features to Spanish Verb
Assign tense = past
Nov 17, 2005 Learning-based MT 77
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
V“no”
Introduced because negation = +
Nov 17, 2005 Learning-based MT 78
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
V“no”
ver
Nov 17, 2005 Learning-based MT 79
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
V“no”
vervi
person = 1number = sgmood = indicativetense = past
Nov 17, 2005 Learning-based MT 80
V
pe
Transfer to Spanish: Top-Down
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
V“no”
vi N
María
N
Pass features over to Spanish side
Nov 17, 2005 Learning-based MT 81
V
pe
I Didn’t see Maria
VSuff
la
VSuffG VSuff
fi
VSuffG VSuff
ñ
VSuffG
NP
N
Maria
N
S
V
VP
S
VP
NP“a”V
V“no”
vi N
María
N
Nov 17, 2005 Learning-based MT 83
Conclusions• Transfer rules (both manual and learned) offer
significant contributions that can complement existing data-driven approaches– Also in medium and large data settings?
• Initial steps to development of a statistically grounded transfer-based MT system with:– Rules that are scored based on a well-founded
probability model – Strong and effective decoding that incorporates the
most advanced techniques used in SMT decoding
• Working from the “opposite” end of research on incorporating models of syntax into “standard” SMT systems [Knight et al]
• Our direction makes sense in the limited data scenario
Nov 17, 2005 Learning-based MT 84
Missing Science
• Monolingual learning tasks:– Learning morphology: morphemes and their meaning– Learning syntactic and semantic structures:
grammar induction• Bilingual Learning Tasks:
– Automatic acquisition of word and phrase translation lexicons
– Learning structural mappings (syntactic, semantic, non-compositional)
• Models that effectively combine learned symbolic knowledge with statistical information: new “decoders”
Nov 17, 2005 Learning-based MT 85
AVENUE PartnersLanguage Country Institutions
Mapudungun (in place)
Chile Universidad de la Frontera, Institute for Indigenous Studies, Ministry of Education
Quechua(discussion)
Peru Ministry of Education
Aymara(discussion)
Bolivia, Peru Ministry of Education
Nov 17, 2005 Learning-based MT 86
The Transfer EngineAnalysis
Source text is parsed into its grammatical structure. Determines transfer application ordering.
Example:
他 看 书。 (he read book)
S
NP VP
N V NP
他 看 书
TransferA 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.
GenerationTarget 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”
Nov 17, 2005 Learning-based MT 87
Seeded VSL: Some Open Issues
• Three types of constraints:– X-side constrain applicability of rule– Y-side assist in generation– X-Y transfer features from SL to TL
• Which of the three types improves translation performance?– Use rules without features to populate lattice, decoder will select
the best translation…– Learn only X-Y constraints, based on list of universal projecting
features• Other notions of version-spaces of feature constraints:
– Current feature learning is specific to rules that have identical transfer components
– Important issue during transfer is to disambiguate among rules that have same SL side but different TL side – can we learn effective constraints for this?
Nov 17, 2005 Learning-based MT 88
Examples of Learned Rules (Hindi-to-English)
{NP,14244}
;;Score:0.0429
NP::NP [N] -> [DET N]
(
(X1::Y2)
)
{NP,14434}
;;Score:0.0040
NP::NP [ADJ CONJ ADJ N] ->
[ADJ CONJ ADJ N]
(
(X1::Y1) (X2::Y2)
(X3::Y3) (X4::Y4)
)
{PP,4894};;Score:0.0470PP::PP [NP POSTP] -> [PREP NP]((X2::Y1)(X1::Y2))
Nov 17, 2005 Learning-based MT 89
Transfer Engine
English Language Model
Transfer Rules{NP1,3}NP1::NP1 [NP1 "H" ADJ] -> [ADJ NP1]((X3::Y1) (X1::Y2) ((X1 def) = +) ((X1 status) =c absolute) ((X1 num) = (X3 num)) ((X1 gen) = (X3 gen)) (X0 = X1))
Translation Lexicon
N::N |: ["$WR"] -> ["BULL"]((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "BULL"))
N::N |: ["$WRH"] -> ["LINE"]((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "LINE"))
Hebrew Input
בשורה הבאה
Decoder
English Output
in the next line
Translation Output Lattice
(0 1 "IN" @PREP)(1 1 "THE" @DET)(2 2 "LINE" @N)(1 2 "THE LINE" @NP)(0 2 "IN LINE" @PP)(0 4 "IN THE NEXT LINE" @PP)
Preprocessing
Morphology
Nov 17, 2005 Learning-based MT 90
Future Directions• Continued work on automatic rule learning (especially
Seeded Version Space Learning)– Use Hebrew and Hindi systems as test platforms for
experimenting with advanced learning research• Rule Refinement via interaction with bilingual speakers• Developing a well-founded model for assigning scores
(probabilities) to transfer rules• Redesigning and improving decoder to better fit the
specific characteristics of the XFER model• Improved leveraging from manual grammar resources• MEMT with improved
– Combination of output from different translation engines with different confidence scores
– strong decoding capabilities
Nov 17, 2005 Learning-based MT 91
Seeded Version Space Learning
NP v det n NP VP …1. Group seed rules into version spaces as above.2. Make use of partial order of rules in version space. Partial order is defined
via the f-structures satisfying the constraints.3. Generalize in the space by repeated merging of rules:
1. Deletion of constraint2. Moving value constraints to agreement constraints, e.g.
((x1 num) = *pl), ((x3 num) = *pl) ((x1 num) = (x3 num)
4. Check translation power of generalized rules against sentence pairs
Nov 17, 2005 Learning-based MT 92
Seeded Version Space Learning:The Search
• The Seeded Version Space algorithm itself is the repeated generalization of rules by merging
• A merge is successful if the set of sentences that can correctly be translated with the merged rule is a superset of the union of sets that can be translated with the unmerged rules, i.e. check power of rule
• Merge until no more successful merges
Nov 17, 2005 Learning-based MT 93
AVENUE Architecture
User
Learning Module
ElicitationProcess
TransferRule
Learning
TransferRules
Run-Time Module
SLInput
SL Parser
TransferEngine
TLGenerator
TLOutputDecoder
MorphologyPre-proc
Nov 17, 2005 Learning-based MT 94
Learning Transfer-Rules for Languages with Limited Resources
• Rationale:– Large bilingual corpora not available– Bilingual native informant(s) can translate and align a
small pre-designed elicitation corpus, using elicitation tool– Elicitation corpus designed to be typologically
comprehensive and compositional– Transfer-rule engine and new learning approach support
acquisition of generalized transfer-rules from the data
Nov 17, 2005 Learning-based MT 95
The Transfer EngineAnalysis
Source text is parsed into its grammatical structure. Determines transfer application ordering.
Example:
他 看 书。 (he read book)
S
NP VP
N V NP
他 看 书
TransferA 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.
GenerationTarget 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”
Nov 17, 2005 Learning-based MT 96
Transfer Rule Formalism
Type informationPart-of-speech/constituent
informationAlignments
x-side constraints
y-side constraints
xy-constraints, e.g. ((Y1 AGR) = (X1 AGR))
;SL: the man, TL: der Mann
NP::NP [DET N] -> [DET N]((X1::Y1)(X2::Y2)
((X1 AGR) = *3-SING)((X1 DEF = *DEF)((X2 AGR) = *3-SING)((X2 COUNT) = +)
((Y1 AGR) = *3-SING)((Y1 DEF) = *DEF)((Y2 AGR) = *3-SING)((Y2 GENDER) = (Y1 GENDER)))
Nov 17, 2005 Learning-based MT 97
Transfer Rule Formalism (II)
Value constraints
Agreement constraints
;SL: the man, TL: der MannNP::NP [DET N] -> [DET N]((X1::Y1)(X2::Y2)
((X1 AGR) = *3-SING)((X1 DEF = *DEF)((X2 AGR) = *3-SING)((X2 COUNT) = +)
((Y1 AGR) = *3-SING)((Y1 DEF) = *DEF)((Y2 AGR) = *3-SING)((Y2 GENDER) = (Y1 GENDER)))
Nov 17, 2005 Learning-based MT 98
Rule Learning - Overview
• Goal: Acquire Syntactic Transfer Rules• Use available knowledge from the source
side (grammatical structure)• Three steps:
1. Flat Seed Generation: first guesses at transfer rules; flat syntactic structure
2. Compositionality: use previously learned rules to add hierarchical structure
3. Seeded Version Space Learning: refine rules by generalizing with validation (learn appropriate feature constraints)
Nov 17, 2005 Learning-based MT 99
Examples of Learned Rules (I){NP,14244}
;;Score:0.0429
NP::NP [N] -> [DET N]
(
(X1::Y2)
)
{NP,14434}
;;Score:0.0040
NP::NP [ADJ CONJ ADJ N] ->
[ADJ CONJ ADJ N]
(
(X1::Y1) (X2::Y2)
(X3::Y3) (X4::Y4)
)
{PP,4894};;Score:0.0470PP::PP [NP POSTP] -> [PREP NP]((X2::Y1)(X1::Y2))
Nov 17, 2005 Learning-based MT 100
A Limited Data Scenario for Hindi-to-English
• Put together a scenario with “miserly” data resources:– Elicited Data corpus: 17589 phrases– Cleaned portion (top 12%) of LDC dictionary: ~2725
Hindi words (23612 translation pairs)– Manually acquired resources during the SLE:
• 500 manual bigram translations• 72 manually written phrase transfer rules• 105 manually written postposition rules• 48 manually written time expression rules
• No additional parallel text!!
Nov 17, 2005 Learning-based MT 101
Manual Grammar Development
• Covers mostly NPs, PPs and VPs (verb complexes)
• ~70 grammar rules, covering basic and recursive NPs and PPs, verb complexes of main tenses in Hindi (developed in two weeks)
Nov 17, 2005 Learning-based MT 102
Manual Transfer Rules: Example;; PASSIVE OF SIMPLE PAST (NO AUX) WITH LIGHT VERB;; passive of 43 (7b){VP,28}VP::VP : [V V V] -> [Aux V]( (X1::Y2) ((x1 form) = root) ((x2 type) =c light) ((x2 form) = part) ((x2 aspect) = perf) ((x3 lexwx) = 'jAnA') ((x3 form) = part) ((x3 aspect) = perf) (x0 = x1) ((y1 lex) = be) ((y1 tense) = past) ((y1 agr num) = (x3 agr num)) ((y1 agr pers) = (x3 agr pers)) ((y2 form) = part))
Nov 17, 2005 Learning-based MT 103
Manual Transfer Rules: Example
; NP1 ke NP2 -> NP2 of NP1; Ex: jIvana ke eka aXyAya; life of (one) chapter ; ==> a chapter of life;{NP,12}NP::NP : [PP NP1] -> [NP1 PP]( (X1::Y2) (X2::Y1); ((x2 lexwx) = 'kA'))
{NP,13}NP::NP : [NP1] -> [NP1]( (X1::Y1))
{PP,12}PP::PP : [NP Postp] -> [Prep NP]( (X1::Y2) (X2::Y1))
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
Nov 17, 2005 Learning-based MT 104
Adding a “Strong” Decoder
• XFER system produces a full lattice• Edges are scored using word-to-word
translation probabilities, trained from the limited bilingual data
• Decoder uses an English LM (70m words)• Decoder can also reorder words or phrases (up
to 4 positions ahead)• For XFER(strong) , ONLY edges from basic XFER
system are used!
Nov 17, 2005 Learning-based MT 105
Testing Conditions
• Tested on section of JHU provided data: 258 sentences with four reference translations– SMT system (stand-alone)– EBMT system (stand-alone)– XFER system (naïve decoding)– XFER system with “strong” decoder
• No grammar rules (baseline)• Manually developed grammar rules• Automatically learned grammar rules
– XFER+SMT with strong decoder (MEMT)
Nov 17, 2005 Learning-based MT 106
Results on JHU Test Set (very miserly training data)System BLEU M-BLEU NIST
EBMT 0.058 0.165 4.22
SMT 0.093 0.191 4.64
XFER (naïve) man grammar
0.055 0.177 4.46
XFER (strong)
no grammar0.109 0.224 5.29
XFER (strong) learned grammar
0.116 0.231 5.37
XFER (strong) man grammar
0.135 0.243 5.59
XFER+SMT 0.136 0.243 5.65
Nov 17, 2005 Learning-based MT 107
Effect of Reordering in the Decoder
NIST vs. Reordering
4.8
4.9
5
5.1
5.2
5.3
5.4
5.5
5.6
5.7
0 1 2 3 4
reordering window
NIS
T s
core no grammar
learned grammar
manual grammar
MEMT: SFXER+ SMT
Nov 17, 2005 Learning-based MT 108
Observations and Lessons (I)• XFER with strong decoder outperformed SMT even
without any grammar rules in the miserly data scenario– SMT Trained on elicited phrases that are very short– SMT has insufficient data to train more discriminative
translation probabilities– XFER takes advantage of Morphology
• Token coverage without morphology: 0.6989• Token coverage with morphology: 0.7892
• Manual grammar currently somewhat better than automatically learned grammar– Learned rules did not yet use version-space learning– Large room for improvement on learning rules – Importance of effective well-founded scoring of learned rules
Nov 17, 2005 Learning-based MT 109
Observations and Lessons (II)
• MEMT (XFER and SMT) based on strong decoder produced best results in the miserly scenario.
• Reordering within the decoder provided very significant score improvements– Much room for more sophisticated grammar rules– Strong decoder can carry some of the reordering
“burden”
Nov 17, 2005 Learning-based MT 110
Conclusions• Transfer rules (both manual and learned) offer
significant contributions that can complement existing data-driven approaches– Also in medium and large data settings?
• Initial steps to development of a statistically grounded transfer-based MT system with:– Rules that are scored based on a well-founded
probability model – Strong and effective decoding that incorporates the
most advanced techniques used in SMT decoding
• Working from the “opposite” end of research on incorporating models of syntax into “standard” SMT systems [Knight et al]
• Our direction makes sense in the limited data scenario
Nov 17, 2005 Learning-based MT 111
Future Directions• Continued work on automatic rule learning
(especially Seeded Version Space Learning)• Improved leveraging from manual grammar
resources, interaction with bilingual speakers• Developing a well-founded model for assigning
scores (probabilities) to transfer rules• Improving the strong decoder to better fit the
specific characteristics of the XFER model• MEMT with improved
– Combination of output from different translation engines with different scorings
– strong decoding capabilities
Nov 17, 2005 Learning-based MT 112
Rule Learning - Overview
• Goal: Acquire Syntactic Transfer Rules• Use available knowledge from the source
side (grammatical structure)• Three steps:
1. Flat Seed Generation: first guesses at transfer rules; no syntactic structure
2. Compositionality: use previously learned rules to add structure
3. Seeded Version Space Learning: refine rules by generalizing with validation
Nov 17, 2005 Learning-based MT 113
Flat Seed Generation
Create a transfer rule that is specific to the sentence pair, but abstracted to the POS level. No syntactic structure.
Element Source
SL POS sequence f-structure
TL POS sequence TL dictionary, aligned SL words
Type information corpus, same on SL and TL
Alignments informant
x-side constraints f-structure
y-side constraints TL dictionary, aligned SL words (list of projecting features)
Nov 17, 2005 Learning-based MT 114
Flat Seed Generation - Example
The highly qualified applicant did not accept the offer.Der äußerst qualifizierte Bewerber nahm das Angebot nicht an.
((1,1),(2,2),(3,3),(4,4),(6,8),(7,5),(7,9),(8,6),(9,7))
S::S [det adv adj n aux neg v det n] -> [det adv adj n v det n neg vpart](;;alignments:(x1:y1)(x2::y2)(x3::y3)(x4::y4)(x6::y8)(x7::y5)(x7::y9)(x8::y6)(x9::y7));;constraints:((x1 def) = *+) ((x4 agr) = *3-sing) ((x5 tense) = *past) …. ((y1 def) = *+) ((y3 case) = *nom) ((y4 agr) = *3-sing) …. )
Nov 17, 2005 Learning-based MT 115
Compositionality - Overview
• Traverse the c-structure of the English sentence, add compositional structure for translatable chunks
• Adjust constituent sequences, alignments• Remove unnecessary constraints, i.e. those
that are contained in the lower-level rule• Adjust constraints: use f-structure of correct
translation vs. f-structure of incorrect translations to introduce context constraints
Nov 17, 2005 Learning-based MT 116
Compositionality - Example
S::S [det adv adj n aux neg v det n] -> [det adv adj n v det n neg vpart](;;alignments:(x1:y1)(x2::y2)(x3::y3)(x4::y4)(x6::y8)(x7::y5)(x7::y9)(x8::y6)(x9::y7));;constraints:((x1 def) = *+) ((x4 agr) = *3-sing) ((x5 tense) = *past) …. ((y1 def) = *+) ((y3 case) = *nom) ((y4 agr) = *3-sing) …. )
S::S [NP aux neg v det n] -> [NP v det n neg vpart](;;alignments:(x1::y1)(x3::y5)(x4::y2)(x4::y6)(x5::y3)(x6::y4);;constraints:((x2 tense) = *past) …. ((y1 def) = *+) ((y1 case) = *nom) …. )
NP::NP [det AJDP n]-> [det ADJP n]
((x1::y1)…((y3 agr) = *3-sing)((x3 agr = *3-sing)
….)
Nov 17, 2005 Learning-based MT 117
Seeded Version Space Learning: Overview
• Goal: further generalize the acquired rules• Methodology:
– Preserve general structural transfer– Consider relaxing specific feature constraints
• 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
Nov 17, 2005 Learning-based MT 118
Seeded Version Space Learning
NP v det n NP VP …1. Group seed rules into version spaces as above.2. Make use of partial order of rules in version space. Partial order is defined
via the f-structures satisfying the constraints.3. Generalize in the space by repeated merging of rules:
1. Deletion of constraint2. Moving value constraints to agreement constraints, e.g.
((x1 num) = *pl), ((x3 num) = *pl) ((x1 num) = (x3 num)
4. Check translation power of generalized rules against sentence pairs
Nov 17, 2005 Learning-based MT 119
Seeded Version Space Learning: Example
S::S [NP aux neg v det n] -> [NP v det n neg vpart](;;alignments:(x1::y1)(x3::y5)(x4::y2)(x4::y6)(x5::y3)(x6::y4);;constraints:((x2 tense) = *past) …. ((y1 def) = *+) ((y1 case) = *nom) ((y1 agr) = *3-sing) … )((y3 agr) = *3-sing) ((y4 agr) = *3-sing)… )
S::S [NP aux neg v det n] -> [NP v det n neg vpart](;;alignments:(x1::y1)(x3::y5)(x4::y2)(x4::y6)(x5::y3)(x6::y4);;constraints:((x2 tense) = *past) …((y1 def) = *+) ((y1 case) = *nom) ((y1 agr) = *3-plu) …((y3 agr) = *3-plu) ((y4 agr) = *3-plu)… )
S::S[NP aux neg v det n] -> [NP n det n neg vpart](;;alignments:(x1::y1)(x3::y5)(x4::y2)(x4::y6)(x5::y3)(x6::y4);;constraints:((x2 tense) = *past) …((y1 def) = *+) ((y1 case) = *nom)((y4 agr) = (y3 agr))… )
Nov 17, 2005 Learning-based MT 120
Preliminary Evaluation
• English to German• Corpus of 141 ADJPs, simple NPs and
sentences• 10-fold cross-validation experiment• Goals:
– Do we learn useful transfer rules?– Does Compositionality improve
generalization?– Does VS-learning improve generalization?
Nov 17, 2005 Learning-based MT 121
Summary of Results
• Average translation accuracy on cross-validation test set was 62%
• Without VS-learning: 43%• Without Compositionality: 57%• Average number of VSs: 24• Average number of sents per VS: 3.8• Average number of merges per VS: 1.6• Percent of compositional rules: 34%
Nov 17, 2005 Learning-based MT 122
Conclusions
• New paradigm for learning transfer rules from pre-designed elicitation corpus
• Geared toward languages with very limited resources
• Preliminary experiments validate approach: compositionality and VS-learning improve generalization
Nov 17, 2005 Learning-based MT 123
Future Work
1. Larger, more diverse elicitation corpus2. Additional languages (Mapudungun…)3. Less information on TL side4. Reverse translation direction5. Refine the various algorithms:
• Operators for VS generalization• Generalization VS search• Layers for compositionality
6. User interactive verification
Nov 17, 2005 Learning-based MT 124
Seeded Version Space 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))
Nov 17, 2005 Learning-based MT 125
Seeded Version Space Learning: Merging Two Rules
Merging algorithm proceeds in three steps. To merge tr1 and tr2 into trmerged:
1. Copy all constraints that are both in tr1 and tr2 into trmerged
2. Consider tr1 and tr2 separately. For the remaining constraints in tr1 and tr2 , perform all possible instances of raising value constraints to agreement constraints.
3. Repeat step 1.
Nov 17, 2005 Learning-based MT 126
Seeded Version Space Learning:The Search
• The Seeded Version Space algorithm itself is the repeated generalization of rules by merging
• A merge is successful if the set of sentences that can correctly be translated with the merged rule is a superset of the union of sets that can be translated with the unmerged rules, i.e. check power of rule
• Merge until no more successful merges
Nov 17, 2005 Learning-based MT 127
Constructing a Network of Candidate Pattern Sets (An Example)
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Nov 17, 2005 Learning-based MT 128
Ø.sblamesolve
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Nov 17, 2005 Learning-based MT 129
Ø.sblamesolve
Ø.s.dblame
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Nov 17, 2005 Learning-based MT 130
Ø.sblamesolve
Ø.s.dblame
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Nov 17, 2005 Learning-based MT 131
Ø.sblamesolve
Ø.s.dblame
sblameroamsolve
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Nov 17, 2005 Learning-based MT 132
Ø.sblamesolve
Ø.s.dblame
sblameroamsolve
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Nov 17, 2005 Learning-based MT 133
Ø.sblamesolve
Ø.s.dblame
sblameroamsolve
e.esblamsolv
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Nov 17, 2005 Learning-based MT 134
Ø.sblamesolve
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Ø.s.dblame
sblameroamsolve
e.esblamsolv
Nov 17, 2005 Learning-based MT 135
Ø.sblamesolve
Example Vocabulary
blame blamed blames roamed
roaming roams solve solves solving
Ø.s.dblame
sblameroamsolve
e.esblamsolv
me.mesbla
Nov 17, 2005 Learning-based MT 136
Add Test to the Generate• Finite state hub searching
algorithm (Johnson and Martin, 2003) can weed out unlikely morpheme boundaries to speed up network generation
m
t
t
i n g
s
Ø
m
t
t
z
r
e
o
s
t
a
y
e
a
a
n gi
Ø
r i e s
t.ting.tsres
retrea
Ø.ing.srest
retreatroam
t.tingres
retrea
Ø.ingrest
retreatretryroam
136
Nov 17, 2005 Learning-based MT 137
as404
huelg, huelguist, incluid,
industri, ...
a.as.o.os43
african, cas, jurídic, l, ...
a.as.o.os.tro1
cas
a.as.os50
afectad, cas, jurídic, l, ...
a.as.o59
cas, citad, jurídic, l, ...
a.o.os105
impuest, indonesi, italian, jurídic, ...
a.as199
huelg, incluid, industri,
inundad, ...
a.os134
impedid, impuest, indonesi,
inundad, ...
as.os68
cas, implicad, inundad, jurídic, ...
a.o214
id, indi, indonesi,
inmediat, ...
as.o85
intern, jurídic, just, l, ...
a.tro2
cas.cen
a1237
huelg, ib, id, iglesi, ...
os534
humorístic, human, hígad,
impedid, ...
o1139
hub, hug, human,
huyend, ...
tro16
catas, ce, cen, cua, ...
as.o.os54
cas, implicad, jurídic, l, ...
o.os268
human, implicad, indici,
indocumentad, ...
Each c-suffix is a random variable with a value equal to the count of the c-stems that occur with that suffix
Use Χ2 Test:
Reject hypothesis: a ┴ as (p-value << 0.005)
Accept hypothesis: a ┴ tro (p-value = 0.2)
137
Nov 17, 2005 Learning-based MT 138
Weight c-stems by:
Length,
Length of longest c-suffix that attaches
Frequency
Currently each c-stem is implicitly weighted equal
a.as.o.os.tro1
cas
a.tro2
cas.cen
tro16
catas, ce, cen, cua, ...
a.as.o.os43
african, cas, jurídic, l, ...
a.as.os50
afectad, cas, jurídic, l, ...
a.as.o59
cas, citad, jurídic, l, ...
a.o.os105
impuest, indonesi, italian, jurídic, ...
a.as199
huelg, incluid, industri,
inundad, ...
a.os134
impedid, impuest, indonesi,
inundad, ...
as.os68
cas, implicad, inundad, jurídic, ...
a.o214
id, indi, indonesi,
inmediat, ...
as.o85
intern, jurídic, just, l, ...
a1237
huelg, ib, id, iglesi, ...
as404
huelg, huelguist, incluid,
industri, ...
os534
humorístic, human, hígad,
impedid, ...
o1139
hub, hug, human,
huyend, ...
as.o.os54
cas, implicad, jurídic, l, ...
o.os268
human, implicad, indici,
indocumentad, ...
138
Nov 17, 2005 Learning-based MT 139
Some schemes absent from this network (i.e. a.os.tro)
Sub-network density:Every descendent of a.as.o.os is in the network—Not true for a.as.o.os.tro
a.as.o.os.tro1
cas
a.tro2
cas.cen
tro16
catas, ce, cen, cua, ...
a.as.o.os43
african, cas, jurídic, l, ...
a.as.os50
afectad, cas, jurídic, l, ...
a.as.o59
cas, citad, jurídic, l, ...
a.o.os105
impuest, indonesi, italian, jurídic, ...
a.as199
huelg, incluid, industri,
inundad, ...
a.os134
impedid, impuest, indonesi,
inundad, ...
as.os68
cas, implicad, inundad, jurídic, ...
a.o214
id, indi, indonesi,
inmediat, ...
as.o85
intern, jurídic, just, l, ...
a1237
huelg, ib, id, iglesi, ...
as404
huelg, huelguist, incluid,
industri, ...
os534
humorístic, human, hígad,
impedid, ...
o1139
hub, hug, human,
huyend, ...
as.o.os54
cas, implicad, jurídic, l, ...
o.os268
human, implicad, indici,
indocumentad, ...
139
Nov 17, 2005 Learning-based MT 140
Word-to-Morpheme Segmentation
• De facto standard measure for unsupervised morphology induction
• Prerequisite for many NLP tasks– Machine Translation– Speech Recognition of highly inflecting
languages
140
Nov 17, 2005 Learning-based MT 141
S
NP VP
VDet N
The trees fell
Los cayeronárboles
S
NP VP
VDet N
The tree fell
El cayóárbol
Subject number marked on:• N-head (es)• dependent Det (El vs. Los), and • governing V (ó vs eron)
((TENSE past) (LEXICAL-ASPECT activity) ... (SUBJ ((NUM sg) (PERSON 3sg) ...)))
((TENSE past) (LEXICAL-ASPECT activity) ... (SUBJ ((NUM pl) (PERSON 3sg) ...)))
Nov 17, 2005 Learning-based MT 142
Ø.ed.ly11cleardirectpresentquiet…
Ø.ed.ing.ly6clearopenpresentTotal
ed.ly12bodiclearcorrectquiet…
Ø.ed.ing.ly.s4clearopen…
Ø.ed.ing201aidcleardefenddeliver…
d.ded.ding27aiboardefen…
d.ded.ding.ds19adboardefen…
Ø.ed.ing.s106cleardefendopenpresent…
Morphology LearningAVENUE Approach:
•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
Nov 17, 2005 Learning-based MT 143
a.as.o.os43
africancasjurídicl...
a.as.i.o.os.sandra.tanier.ter.tro.trol
1cas
a.as.os50
afectadcasjurídicl...
a.as.o59cascitadjurídicl...
a.o.os105impuestindonesiitalianjurídic...
a.as199huelgincluidindustriinundad...
a.os134impedidimpuestindonesiinundad...
as.os68cas
implicadinundadjurídic...
a.o214idindi
indonesiinmediat...
as.o85internjurídicjustl...
a.tro2cascen
a1237huelgibidiglesi...
as404huelghuelguistincluidindustri...
os534
humorístichumanhígadimpedid...
o1139hubhughumanhuyend...
tro16catascecencua...
as.o.os54cas
implicadjurídicl...
Figure : Hierarchical scheme lattice automatically derived from a Spanish newswire corpus of 40,011 words and 6,975 unique types.
o.os268humanimplicadindici
indocumentad...