Title: The AVENUE Project: Bootstrapping MT Prototypes for Languages with Limited Resources
1The AVENUE ProjectBootstrapping MT Prototypes
for Languages with Limited Resources
- Faculty
- Alon Lavie, Jaime Carbonell, Lori Levin,
- Ralf Brown
-
- Students
- Katharina Probst, Erik Peterson, Christian
Monson, Ariadna Font-Llitjos, Alison Alvarez
2Why Machine Translation for Minority and
Indigenous Languages?
- Commercial MT economically feasible for only a
handful of major languages with large resources
(corpora, human developers) - Is there hope for MT for languages with limited
resources? - Benefits include
- Better government access to indigenous
communities (Epidemics, crop failures, etc.) - Better indigenous communities participation in
information-rich activities (health care,
education, government) without giving up their
languages. - Language preservation
- Civilian and military applications (disaster
relief)
3MT for Minority and Indigenous Languages
Challenges
- Minimal amount of parallel text
- Possibly competing standards for
orthography/spelling - Often relatively few trained linguists
- Access to native informants possible
- Need to minimize development time and cost
4AVENUE Partners
5AVENUE 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 - Rule Refinement refine the acquired rules via a
process of interaction with bilingual informants - Morphology Learning
- Word and Phrase bilingual lexicon acquisition
6AVENUE Architecture
7Learning 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
8English-Chinese Example
9English-Hindi Example
10Spanish-Mapudungun Example
11English-Arabic Example
12Transfer 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))
13Transfer 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
14The Transfer Engine
15Rule 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 use previously learned rules to
add hierarchical structure - Seeded Version Space Learning refine rules by
learning appropriate feature constraints
16Flat Seed Rule Generation
17Compositionality
18Seeded Version Space Learning
19Seeded 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?
20Examples of Learned Rules (Hindi-to-English)
21XFER 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
22(No Transcript)
23Morphology Example
- Input word BWRH
- 0 1 2 3 4
- --------BWRH--------
- -----B-----WR--H--
- --B---H----WRH---
-
24Morphology 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)
25Sample 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
26Evaluation Results
- Test set of 62 sentences from Haaretz newspaper,
2 reference translations
27Future 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
28Flat Seed Generation
- Create a transfer rule that is specific to the
sentence pair, but abstracted to the POS level.
No syntactic structure.
29Compositionality - 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
30Seeded 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 -
31Seeded 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))
32Seeded Version Space Learning
-
-
- NP v det n NP VP
- Group seed rules into version spaces as above.
- Make use of partial order of rules in version
space. Partial order is defined - via the f-structures satisfying the constraints.
- Generalize in the space by repeated merging of
rules - Deletion of constraint
- 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
33Seeded Version Space LearningThe 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
34Conclusions
- 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
35AVENUE Architecture
Run-Time Module
Learning Module
SL Input
SL Parser
Morphology Pre-proc
Elicitation Process
Transfer Rule Learning
Transfer Rules
Transfer Engine
TL Output
TL Generator
Decoder
User
36Learning 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
37The 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
38The Transfer Engine
39Transfer Rule Formalism
SL the man, TL der Mann NPNP DET N -gt
DET N ( (X1Y1) (X2Y2) ((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)) )
- Type information
- Part-of-speech/constituent information
- Alignments
- x-side constraints
- y-side constraints
- xy-constraints,
- e.g. ((Y1 AGR) (X1 AGR))
40Transfer Rule Formalism (II)
SL the man, TL der Mann NPNP DET N -gt
DET N ( (X1Y1) (X2Y2) ((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)) )
- Value constraints
-
- Agreement constraints
41Rule 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 use previously learned rules to
add hierarchical structure - Seeded Version Space Learning refine rules by
generalizing with validation (learn appropriate
feature constraints)
42Examples of Learned Rules (I)
43A 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!!
44Manual 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)
45Manual Transfer Rules Example
PASSIVE OF SIMPLE PAST (NO AUX) WITH LIGHT
VERB passive of 43 (7b) VP,28 VPVP V V
V -gt Aux V ( (X1Y2) ((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) )
46Manual Transfer Rules Example
NP PP NP1 NP P Adj N
N1 ke eka aXyAya N
jIvana
NP NP1 PP Adj N
P NP one chapter of N1
N life
NP1 ke NP2 -gt NP2 of NP1 Ex jIvana ke
eka aXyAya life of (one) chapter
gt a chapter of life NP,12 NPNP PP
NP1 -gt NP1 PP ( (X1Y2) (X2Y1) ((x2
lexwx) 'kA') ) NP,13 NPNP NP1 -gt
NP1 ( (X1Y1) ) PP,12 PPPP NP Postp
-gt Prep NP ( (X1Y2) (X2Y1) )
47Adding 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!
48Testing 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
- XFERSMT with strong decoder (MEMT)
49Results on JHU Test Set (very miserly training
data)
50Effect of Reordering in the Decoder
51Observations 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
52Observations 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
53Conclusions
- 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
54Future 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
55Rule 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 no syntactic structure - Compositionality use previously learned rules to
add structure - Seeded Version Space Learning refine rules by
generalizing with validation
56Flat Seed Generation
- Create a transfer rule that is specific to the
sentence pair, but abstracted to the POS level.
No syntactic structure.
57Flat 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))
SS det adv adj n aux neg v det n -gt det adv
adj n v det n neg vpart (alignments (x1y1)(x2
y2)(x3y3)(x4y4)(x6y8)(x7y5)(x7y9)(x8
y6)(x9y7)) constraints ((x1 def) ) ((x4
agr) 3-sing) ((x5 tense) past) . ((y1
def) ) ((y3 case) nom) ((y4 agr)
3-sing) . )
58Compositionality - 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
59Compositionality - Example
SS det adv adj n aux neg v det n -gt det adv
adj n v det n neg vpart (alignments (x1y1)(x2
y2)(x3y3)(x4y4)(x6y8)(x7y5)(x7y9)(x8
y6)(x9y7)) constraints ((x1 def) ) ((x4
agr) 3-sing) ((x5 tense) past) . ((y1
def) ) ((y3 case) nom) ((y4 agr)
3-sing) . )
NPNP det AJDP n -gt det ADJP
n ((x1y1) ((y3 agr) 3-sing) ((x3 agr
3-sing) .)
SS NP aux neg v det n -gt NP v det n neg
vpart (alignments (x1y1)(x3y5)(x4y2)(x4
y6)(x5y3)(x6y4) constraints ((x2 tense)
past) . ((y1 def) ) ((y1 case) nom) .
)
60Seeded 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 -
61Seeded Version Space Learning
-
-
- NP v det n NP VP
- Group seed rules into version spaces as above.
- Make use of partial order of rules in version
space. Partial order is defined - via the f-structures satisfying the constraints.
- Generalize in the space by repeated merging of
rules - Deletion of constraint
- 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
62Seeded Version Space Learning Example
SS NP aux neg v det n -gt NP v det n neg
vpart (alignments (x1y1)(x3y5)(x4y2)(x4
y6)(x5y3)(x6y4) constraints ((x2 tense)
past) . ((y1 def) ) ((y1 case) nom)
((y1 agr) 3-sing) ) ((y3 agr) 3-sing)
((y4 agr) 3-sing) )
SS NP aux neg v det n -gt NP n det n neg
vpart ( alignments (x1y1)(x3y5) (x4y2)(x
4y6) (x5y3)(x6y4) constraints ((x2
tense) past) ((y1 def) ) ((y1 case)
nom) ((y4 agr) (y3 agr)) )
SS NP aux neg v det n -gt NP v det n neg
vpart (alignments (x1y1)(x3y5)(x4y2)(x4
y6)(x5y3)(x6y4) constraints ((x2 tense)
past) ((y1 def) ) ((y1 case) nom) ((y1
agr) 3-plu) ((y3 agr) 3-plu) ((y4 agr)
3-plu) )
63Preliminary 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?
64Summary 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
65Conclusions
- 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
66Future Work
- Larger, more diverse elicitation corpus
- Additional languages (Mapudungun)
- Less information on TL side
- Reverse translation direction
- Refine the various algorithms
- Operators for VS generalization
- Generalization VS search
- Layers for compositionality
- User interactive verification
67Seeded 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))
68Seeded Version Space Learning Merging Two Rules
- Merging algorithm proceeds in three steps.
- To merge tr1 and tr2 into trmerged
- Copy all constraints that are both in tr1 and tr2
into trmerged - Consider tr1 and tr2 separately. For the
remaining constraints in tr1 and tr2 , perform
all possible instances of raising value
constraints to agreement constraints. - Repeat step 1.
69Seeded Version Space LearningThe 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