Alon Lavie and Satanjeev Banerjee Language Technologies Institute Carnegie Mellon University
Enabling MT for Languages with Limited Resources Alon Lavie Language Technologies Institute Carnegie...
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Enabling MT for Languages with Limited Resources
Alon LavieLanguage Technologies Institute
Carnegie Mellon University
October 5, 2004 TMI 2004 Panel 2
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?
October 5, 2004 TMI 2004 Panel 3
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
October 5, 2004 TMI 2004 Panel 4
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• Effective integration of acquired knowledge
with statistical/distributional information
October 5, 2004 TMI 2004 Panel 5
CMU’s AVENUE Approach• Elicitation: use bilingual native informants to produce a
small high-quality word-aligned bilingual corpus of translated phrases and sentences
• 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 all 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
October 5, 2004 TMI 2004 Panel 6
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
October 5, 2004 TMI 2004 Panel 7
Learning Transfer-Rules for Languages with Limited Resources
• Rationale:– 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
October 5, 2004 TMI 2004 Panel 8
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)))
October 5, 2004 TMI 2004 Panel 9
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 learning appropriate feature constraints
October 5, 2004 TMI 2004 Panel 10
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))
October 5, 2004 TMI 2004 Panel 11
CompositionalityInitial 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))
October 5, 2004 TMI 2004 Panel 12
Seeded Version Space 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))
October 5, 2004 TMI 2004 Panel 13
AVENUE Prototypes
• General XFER framework under development for past two years
• Prototype systems so far:– German-to-English, Spanish-to-English– Hindi-to-English, Hebrew-to-English
• In progress or planned:– Mapudungun-to-Spanish– Quechua-to-Spanish– Arabic-to-English– Native-Brazilian language to Brazilian Portuguese
October 5, 2004 TMI 2004 Panel 14
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 and semantic)
• Models that effectively combine learned symbolic knowledge with statistical information: new “decoders”
October 5, 2004 TMI 2004 Panel 15
October 5, 2004 TMI 2004 Panel 16
English-Chinese Example
October 5, 2004 TMI 2004 Panel 17
English-Hindi Example
October 5, 2004 TMI 2004 Panel 18
Spanish-Mapudungun Example
October 5, 2004 TMI 2004 Panel 19
English-Arabic Example
October 5, 2004 TMI 2004 Panel 20
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)))
October 5, 2004 TMI 2004 Panel 21
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
October 5, 2004 TMI 2004 Panel 22
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”
October 5, 2004 TMI 2004 Panel 23
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?
October 5, 2004 TMI 2004 Panel 24
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))
October 5, 2004 TMI 2004 Panel 25
XFER MT for Hebrew-to-English• Two month intensive effort to apply our XFER approach
to the development of a Hebrew-to-English MT system• Challenges:
– No large parallel corpus– Limited coverage translation lexicon– Rich Morphology: incomplete analyzer available
• Accomplished:– Collected available resources, establish methodology for
processing Hebrew input– 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
October 5, 2004 TMI 2004 Panel 26
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
October 5, 2004 TMI 2004 Panel 27
Morphology Example
• Input word: B$WRH
0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---|
October 5, 2004 TMI 2004 Panel 28
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))
October 5, 2004 TMI 2004 Panel 29
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
October 5, 2004 TMI 2004 Panel 30
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
October 5, 2004 TMI 2004 Panel 31
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
October 5, 2004 TMI 2004 Panel 32
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)
October 5, 2004 TMI 2004 Panel 33
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
October 5, 2004 TMI 2004 Panel 34
Seeded Version Space Learning: Overview
• 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
October 5, 2004 TMI 2004 Panel 35
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))
October 5, 2004 TMI 2004 Panel 36
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
October 5, 2004 TMI 2004 Panel 37
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
October 5, 2004 TMI 2004 Panel 38
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
October 5, 2004 TMI 2004 Panel 39
AVENUE Architecture
User
Learning Module
ElicitationProcess
TransferRule
Learning
TransferRules
Run-Time Module
SLInput
SL Parser
TransferEngine
TLGenerator
TLOutputDecoder
MorphologyPre-proc
October 5, 2004 TMI 2004 Panel 40
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
October 5, 2004 TMI 2004 Panel 41
The Elicitation Corpus
• Translated, aligned by bilingual informant• Corpus consists of linguistically diverse
constructions• Based on elicitation and documentation work
of field linguists (e.g. Comrie 1977, Bouquiaux 1992)
• Organized compositionally: elicit simple structures first, then use them as building blocks
• Goal: minimize size, maximize linguistic coverage
October 5, 2004 TMI 2004 Panel 42
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”
October 5, 2004 TMI 2004 Panel 43
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)))
October 5, 2004 TMI 2004 Panel 44
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)))
October 5, 2004 TMI 2004 Panel 45
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)
October 5, 2004 TMI 2004 Panel 46
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))
October 5, 2004 TMI 2004 Panel 47
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!!
October 5, 2004 TMI 2004 Panel 48
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)
October 5, 2004 TMI 2004 Panel 49
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))
October 5, 2004 TMI 2004 Panel 50
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
October 5, 2004 TMI 2004 Panel 51
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!
October 5, 2004 TMI 2004 Panel 52
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)
October 5, 2004 TMI 2004 Panel 53
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
October 5, 2004 TMI 2004 Panel 54
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
October 5, 2004 TMI 2004 Panel 55
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
October 5, 2004 TMI 2004 Panel 56
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”
October 5, 2004 TMI 2004 Panel 57
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
October 5, 2004 TMI 2004 Panel 58
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
October 5, 2004 TMI 2004 Panel 59
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
October 5, 2004 TMI 2004 Panel 60
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)
October 5, 2004 TMI 2004 Panel 61
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) …. )
October 5, 2004 TMI 2004 Panel 62
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
October 5, 2004 TMI 2004 Panel 63
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)
….)
October 5, 2004 TMI 2004 Panel 64
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
October 5, 2004 TMI 2004 Panel 65
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
October 5, 2004 TMI 2004 Panel 66
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))… )
October 5, 2004 TMI 2004 Panel 67
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?
October 5, 2004 TMI 2004 Panel 68
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%
October 5, 2004 TMI 2004 Panel 69
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
October 5, 2004 TMI 2004 Panel 70
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
October 5, 2004 TMI 2004 Panel 71
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))
October 5, 2004 TMI 2004 Panel 72
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.
October 5, 2004 TMI 2004 Panel 73
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