MT for Languages with Limited
Resources
11-731
Machine Translation
April 20, 2011
Based on Joint Work with: Lori Levin, Jaime
Carbonell, Stephan Vogel, Shuly Wintner, Danny
Shacham, Katharina Probst, Erik Peterson, Christian
Monson, Roberto Aranovich and Ariadna Font-Llitjos
Why 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 very
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)
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MT 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
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MT for Low Resource Languages
• Possible Approaches:
– Phrase-based SMT, with whatever small amounts of
parallel data that is available
– Build a rule-based system – need for bilingual
experts and resources
– Hybrid approaches, such as the AVENUE Project
(Stat-XFER) approach:
• Incorporate acquired manual resources within a
general statistical framework
• Augment with targeted elicitation and resource
acquisition from bilingual non-experts
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CMU Statistical Transfer
(Stat-XFER) MT Approach
• Integrate the major strengths of rule-based and
statistical MT within a common framework:
– Linguistically rich formalism that can express complex and
abstract compositional transfer rules
– Rules can be written by human experts and also acquired
automatically from data
– Easy integration of morphological analyzers and
generators
– Word and syntactic-phrase correspondences can be
automatically acquired from parallel text
– Search-based decoding from statistical MT adapted to find
the best translation within the search space: multi-feature
scoring, beam-search, parameter optimization, etc.
– Framework suitable for both resource-rich and resourcepoor language scenarios
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Stat-XFER Main Principles
• Framework: Statistical search-based approach with
syntactic translation transfer rules that can be acquired
from data but also developed and extended by experts
• Automatic Word and Phrase translation lexicon
acquisition from parallel data
• Transfer-rule Learning: apply ML-based methods to
automatically acquire syntactic transfer rules for
translation between the two languages
• Elicitation: use bilingual native informants to produce a
small high-quality word-aligned bilingual corpus of
translated phrases and sentences
• Rule Refinement: refine the acquired rules via a process
of interaction with bilingual informants
• XFER + Decoder:
– XFER engine produces a lattice of possible transferred
structures at all levels
– Decoder searches and selects the best scoring combination
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Stat-XFER Framework
Source
Input
Preprocessing
Language Weighted
Model
Features
Morphology
Transfer
Rules
Bilingual
Lexicon
April 20, 2011
Transfer
Engine
Translation
Lattice
11-731 Machine Translation
Second-Stage
Decoder
Target
Output
7
Hebrew Input
‫בשורה הבאה‬
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))
Preprocessing
Morphology
English
Language
Model
Transfer
Engine
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"))
Decoder
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)
English Output
in the next line
Transfer Rule Formalism
;SL: the old man, TL: ha-ish ha-zaqen
Type information
Part-of-speech/constituent
information
Alignments
x-side constraints
[DET ADJ N] -> [DET N DET ADJ]
((X1 AGR) = *3-SING)
((X1 DEF = *DEF)
((X3 AGR) = *3-SING)
((X3 COUNT) = +)
y-side constraints
xy-constraints,
e.g. ((Y1 AGR) = (X1 AGR))
April 20, 2011
NP::NP
(
(X1::Y1)
(X1::Y3)
(X2::Y4)
(X3::Y2)
((Y1 DEF) = *DEF)
((Y3 DEF) = *DEF)
((Y2 AGR) = *3-SING)
((Y2 GENDER) = (Y4 GENDER))
)
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Transfer Rule Formalism (II)
;SL: the old man, TL: ha-ish ha-zaqen
NP::NP
(
(X1::Y1)
(X1::Y3)
(X2::Y4)
(X3::Y2)
Value constraints
Agreement constraints
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[DET ADJ N] -> [DET N DET ADJ]
((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))
)
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Translation Lexicon:
Hebrew-to-English Examples
(Semi-manually-developed)
PRO::PRO |: ["ANI"] -> ["I"]
(
(X1::Y1)
((X0 per) = 1)
((X0 num) = s)
((X0 case) = nom)
)
N::N |: ["$&H"] -> ["HOUR"]
(
(X1::Y1)
((X0 NUM) = s)
((Y0 NUM) = s)
((Y0 lex) = "HOUR")
)
PRO::PRO |: ["ATH"] -> ["you"]
(
(X1::Y1)
((X0 per) = 2)
((X0 num) = s)
((X0 gen) = m)
((X0 case) = nom)
)
N::N |: ["$&H"] -> ["hours"]
(
(X1::Y1)
((Y0 NUM) = p)
((X0 NUM) = p)
((Y0 lex) = "HOUR")
)
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Translation Lexicon:
French-to-English Examples
(Automatically-acquired)
DET::DET |: [“le"] -> [“the"]
(
(X1::Y1)
)
NP::NP |: [“le respect"] -> [“accordance"]
(
)
PP::PP |: [“dans le respect"] -> [“in accordance"]
(
)
Prep::Prep |:[“dans”] -> [“in”]
(
(X1::Y1)
)
N::N |: [“principes"] -> [“principles"]
(
(X1::Y1)
)
PP::PP |: [“des principes"] -> [“with the principles"]
(
)
N::N |: [“respect"] -> [“accordance"]
(
(X1::Y1)
)
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Hebrew-English Transfer Grammar
Example Rules
(Manually-developed)
{NP1,2}
;;SL: $MLH ADWMH
;;TL: A RED DRESS
{NP1,3}
;;SL: H $MLWT H ADWMWT
;;TL: THE RED DRESSES
NP1::NP1 [NP1 ADJ] -> [ADJ NP1]
(
(X2::Y1)
(X1::Y2)
((X1 def) = -)
((X1 status) =c absolute)
((X1 num) = (X2 num))
((X1 gen) = (X2 gen))
(X0 = X1)
)
NP1::NP1 [NP1 "H" ADJ] -> [ADJ NP1]
(
(X3::Y1)
(X1::Y2)
((X1 def) = +)
((X1 status) =c absolute)
((X1 num) = (X3 num))
((X1 gen) = (X3 gen))
(X0 = X1)
)
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French-English Transfer Grammar
Example Rules
(Automatically-acquired)
{PP,24691}
;;SL: des principes
;;TL: with the principles
{PP,312}
;;SL: dans le respect des principes
;;TL: in accordance with the principles
PP::PP [“des” N] -> [“with the” N]
(
(X1::Y1)
)
PP::PP [Prep NP] -> [Prep NP]
(
(X1::Y1)
(X2::Y2)
)
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The Transfer Engine
• Input: source-language input sentence, or sourcelanguage confusion network
• Output: lattice representing collection of translation
fragments at all levels supported by transfer rules
• Basic Algorithm: “bottom-up” integrated “parsingtransfer-generation” chart-parser guided by the
synchronous transfer rules
– Start with translations of individual words and phrases
from translation lexicon
– Create translations of larger constituents by applying
applicable transfer rules to previously created lattice
entries
– Beam-search controls the exponential combinatorics of the
search-space, using multiple scoring features
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The Transfer Engine
• Some Unique Features:
– Works with either learned or manually-developed
transfer grammars
– Handles rules with or without unification constraints
– Supports interfacing with servers for morphological
analysis and generation
– Can handle ambiguous source-word analyses and/or
SL segmentations represented in the form of lattice
structures
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Hebrew Example
(From [Lavie et al., 2004])
• Input word: B$WRH
0
1
2
3
4
|--------B$WRH--------|
|-----B-----|$WR|--H--|
|--B--|-H--|--$WRH---|
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Hebrew Example
(From [Lavie et al., 2004])
Y0: ((SPANSTART 0)
(SPANEND 4)
(LEX B$WRH)
(POS N)
(GEN F)
(NUM S)
(STATUS ABSOLUTE))
Y1: ((SPANSTART 0)
(SPANEND 2)
(LEX B)
(POS PREP))
Y2: ((SPANSTART 1)
(SPANEND 3)
(LEX $WR)
(POS N)
(GEN M)
(NUM S)
(STATUS ABSOLUTE))
Y3: ((SPANSTART 3)
(SPANEND 4)
(LEX $LH)
(POS POSS))
Y4: ((SPANSTART 0)
(SPANEND 1)
(LEX B)
(POS PREP))
Y5: ((SPANSTART 1)
(SPANEND 2)
(LEX H)
(POS DET))
Y6: ((SPANSTART 2)
(SPANEND 4)
(LEX $WRH)
(POS N)
(GEN F)
(NUM S)
(STATUS ABSOLUTE))
Y7: ((SPANSTART 0)
(SPANEND 4)
(LEX B$WRH)
(POS LEX))
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XFER Output Lattice
(28
(29
(29
(29
(30
(30
(30
(30
(30
(30
(30
28
29
29
29
30
30
30
30
30
30
30
"AND" -5.6988 "W" "(CONJ,0 'AND')")
"SINCE" -8.20817 "MAZ " "(ADVP,0 (ADV,5 'SINCE')) ")
"SINCE THEN" -12.0165 "MAZ " "(ADVP,0 (ADV,6 'SINCE THEN')) ")
"EVER SINCE" -12.5564 "MAZ " "(ADVP,0 (ADV,4 'EVER SINCE')) ")
"WORKED" -10.9913 "&BD " "(VERB,0 (V,11 'WORKED')) ")
"FUNCTIONED" -16.0023 "&BD " "(VERB,0 (V,10 'FUNCTIONED')) ")
"WORSHIPPED" -17.3393 "&BD " "(VERB,0 (V,12 'WORSHIPPED')) ")
"SERVED" -11.5161 "&BD " "(VERB,0 (V,14 'SERVED')) ")
"SLAVE" -13.9523 "&BD " "(NP0,0 (N,34 'SLAVE')) ")
"BONDSMAN" -18.0325 "&BD " "(NP0,0 (N,36 'BONDSMAN')) ")
"A SLAVE" -16.8671 "&BD " "(NP,1 (LITERAL 'A') (NP2,0 (NP1,0 (NP0,0
(N,34 'SLAVE')) ) ) ) ")
(30 30 "A BONDSMAN" -21.0649 "&BD " "(NP,1 (LITERAL 'A') (NP2,0 (NP1,0
(NP0,0 (N,36 'BONDSMAN')) ) ) ) ")
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The Lattice Decoder
• Stack Decoder, similar to standard Statistical MT
decoders
• Searches for best-scoring path of non-overlapping
lattice arcs
• No reordering during decoding
• Scoring based on log-linear combination of scoring
features, with weights trained using Minimum Error Rate
Training (MERT)
• Scoring components:
–
–
–
–
Statistical Language Model
Bi-directional MLE phrase and rule scores
Lexical Probabilities
Fragmentation: how many arcs to cover the entire
translation?
– Length Penalty: how far from expected target length?
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XFER Lattice Decoder
00
ON THE FOURTH DAY THE LION ATE THE RABBIT TO A MORNING MEAL
Overall: -8.18323, Prob: -94.382, Rules: 0, Frag: 0.153846, Length: 0,
Words: 13,13
235 < 0 8 -19.7602: B H IWM RBI&I (PP,0 (PREP,3 'ON')(NP,2 (LITERAL 'THE')
(NP2,0 (NP1,1 (ADJ,2 (QUANT,0 'FOURTH'))(NP1,0 (NP0,1 (N,6 'DAY')))))))>
918 < 8 14 -46.2973: H ARIH AKL AT H $PN (S,2 (NP,2 (LITERAL 'THE') (NP2,0
(NP1,0 (NP0,1 (N,17 'LION')))))(VERB,0 (V,0 'ATE'))(NP,100
(NP,2 (LITERAL 'THE') (NP2,0 (NP1,0 (NP0,1 (N,24 'RABBIT')))))))>
584 < 14 17 -30.6607: L ARWXH BWQR (PP,0 (PREP,6 'TO')(NP,1 (LITERAL 'A')
(NP2,0 (NP1,0 (NNP,3 (NP0,0 (N,32 'MORNING'))(NP0,0 (N,27 'MEAL')))))))>
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Stat-XFER MT Systems
• General Stat-XFER framework under development for past
nine years
• Systems so far:
–
–
–
–
–
–
–
–
–
–
–
–
–
Chinese-to-English
French-to-English
Hebrew-to-English
Urdu-to-English
German-to-English
Hindi-to-English
Dutch-to-English
Turkish-to-English
Mapudungun-to-Spanish
Arabic-to-English
Brazilian Portuguese-to-English
English-to-Arabic
Hebrew-to-Arabic
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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 rule learning approach support
acquisition of generalized transfer-rules from the data
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English-Chinese Example
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English-Hindi Example
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Spanish-Mapudungun
Example
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English-Arabic Example
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The Typological 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
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The Structural Elicitation Corpus
• Designed to cover the most common phrase
structures of English  learn how these
structures map onto their equivalents in other
languages
• Constructed using the constituent parse trees
from the Penn TreeBank
– Extracted and frequency ranked all rules in parse
trees
– Selected top ~200 rules, filtered idiosyncratic cases
– Revised lexical choices within examples
• Goal: minimize size, maximize linguistic
coverage of structures
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The Structural Elicitation Corpus
Examples:
srcsent: in the forest
tgtsent: B H I&R
aligned: ((1,1),(2,2),(3,3))
context: C-Structure:(<PP> (PREP in-1) (<NP> (DET the-2) (N forest-3)))
srcsent: steps
tgtsent: MDRGWT
aligned: ((1,1))
context: C-Structure:(<NP> (N steps-1))
srcsent: the boy ate the apple
tgtsent: H ILD AKL AT H TPWX
aligned: ((1,1),(2,2),(3,3),(4,5),(5,6))
context: C-Structure:(<S> (<NP> (DET the-1) (N boy-2))
(<VP> (V ate-3) (<NP> (DET the-4)(N apple-5))))
srcsent: the first year
tgtsent: H $NH H RA$WNH
aligned: ((1,1 3),(2,4),(3,2))
context: C-Structure:(<NP> (DET the-1) (<ADJP> (ADJ first-2)) (N year-3))
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A Limited Data Scenario for
Hindi-to-English
• Conducted during a DARPA “Surprise
Language Exercise” (SLE) in June 2003
• 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!!
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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)
)
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{PP,4894}
;;Score:0.0470
PP::PP [NP POSTP] -> [PREP NP]
(
(X2::Y1)
(X1::Y2)
)
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Manual Transfer Rules: Hindi 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)
)
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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
NP
PP
NP1
NP1
NP P
Adj N
Adj N
N1 ke eka aXyAya
PP
P
one chapter of N1
N
N
jIvana
life
{NP,13}
NP::NP : [NP1] -> [NP1]
(
(X1::Y1)
)
{PP,12}
PP::PP : [NP Postp] -> [Prep NP]
(
(X1::Y2)
(X2::Y1)
)
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34
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)
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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)
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Results on JHU Test Set
System
BLEU
M-BLEU
NIST
EBMT
0.058
0.165
4.22
SMT
0.093
0.191
4.64
0.055
0.177
4.46
0.109
0.224
5.29
XFER
0.116
0.231
5.37
XFER
0.135
0.243
5.59
XFER+SMT
0.136
0.243
5.65
XFER
(naïve)
man grammar
XFER
(strong)
no grammar
(strong)
learned grammar
(strong)
man grammar
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Effect of Reordering in the
Decoder
NIST vs. Reordering
5.7
5.6
5.5
NIST score
5.4
no grammar
learned grammar
manual grammar
MEMT: SFXER+ SMT
5.3
5.2
5.1
5
4.9
4.8
0
1
2
3
4
reordering window
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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 was somewhat better than
automatically learned grammar
– Learned rules were very simple
– Large room for improvement on learning rules
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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”
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Modern Hebrew
• Native language of about 3-4 Million in Israel
• Semitic language, closely related to Arabic and
with similar linguistic properties
– Root+Pattern word formation system
– Rich verb and noun morphology
– Particles attach as prefixed to the following word:
definite article (H), prepositions (B,K,L,M),
coordinating conjuction (W), relativizers ($,K$)…
• Unique alphabet and Writing System
– 22 letters represent (mostly) consonants
– Vowels represented (mostly) by diacritics
– Modern texts omit the diacritic vowels, thus
additional level of ambiguity: “bare” word  word
– Example: MHGR  mehager, m+hagar, m+h+ger
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Modern Hebrew Spelling
• Two main spelling variants
– “KTIV XASER” (difficient): spelling with the vowel
diacritics, and consonant words when the diacritics
are removed
– “KTIV MALEH” (full): words with I/O/U vowels are
written with long vowels which include a letter
• KTIV MALEH is predominant, but not strictly
adhered to even in newspapers and official
publications  inconsistent spelling
• Example:
– niqud (spelling): NIQWD, NQWD, NQD
– When written as NQD, could also be niqed, naqed,
nuqad
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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
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Morphological Analyzer
• We use a publicly available morphological
analyzer distributed by the Technion’s
Knowledge Center, adapted for our system
• Coverage is reasonable (for nouns, verbs and
adjectives)
• Produces all analyses or a disambiguated
analysis for each word
• Output format includes lexeme (base form),
POS, morphological features
• Output was adapted to our representation
needs (POS and feature mappings)
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Morphology Example
• Input word: B$WRH
0
1
2
3
4
|--------B$WRH--------|
|-----B-----|$WR|--H--|
|--B--|-H--|--$WRH---|
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Morphology Example
Y0: ((SPANSTART 0)
(SPANEND 4)
(LEX B$WRH)
(POS N)
(GEN F)
(NUM S)
(STATUS ABSOLUTE))
Y1: ((SPANSTART 0)
(SPANEND 2)
(LEX B)
(POS PREP))
Y2: ((SPANSTART 1)
(SPANEND 3)
(LEX $WR)
(POS N)
(GEN M)
(NUM S)
(STATUS ABSOLUTE))
Y3: ((SPANSTART 3)
(SPANEND 4)
(LEX $LH)
(POS POSS))
Y4: ((SPANSTART 0)
(SPANEND 1)
(LEX B)
(POS PREP))
Y5: ((SPANSTART 1)
(SPANEND 2)
(LEX H)
(POS DET))
Y6: ((SPANSTART 2)
(SPANEND 4)
(LEX $WRH)
(POS N)
(GEN F)
(NUM S)
(STATUS ABSOLUTE))
Y7: ((SPANSTART 0)
(SPANEND 4)
(LEX B$WRH)
(POS LEX))
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Translation Lexicon
• Constructed our own Hebrew-to-English lexicon, based
primarily on existing “Dahan” H-to-E and E-to-H
dictionary made available to us, augmented by other
public sources
• Coverage is not great but not bad as a start
– Dahan H-to-E is about 15K translation pairs
– Dahan E-to-H is about 7K translation pairs
• Base forms, POS information on both sides
• Converted Dahan into our representation, added entries
for missing closed-class entries (pronouns, prepositions,
etc.)
• Had to deal with spelling conventions
• Recently augmented with ~50K translation pairs
extracted from Wikipedia (mostly proper names and
named entities)
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Manual Transfer Grammar
(human-developed)
• Initially developed by Alon in a couple of days,
extended and revised by Nurit over time
• Current grammar has 36 rules:
–
–
–
–
21 NP rules
one PP rule
6 verb complexes and VP rules
8 higher-phrase and sentence-level rules
• Captures the most common (mostly local)
structural differences between Hebrew and
English
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Transfer Grammar
Example Rules
{NP1,2}
;;SL: $MLH ADWMH
;;TL: A RED DRESS
{NP1,3}
;;SL: H $MLWT H ADWMWT
;;TL: THE RED DRESSES
NP1::NP1 [NP1 ADJ] -> [ADJ NP1]
(
(X2::Y1)
(X1::Y2)
((X1 def) = -)
((X1 status) =c absolute)
((X1 num) = (X2 num))
((X1 gen) = (X2 gen))
(X0 = X1)
)
NP1::NP1 [NP1 "H" ADJ] -> [ADJ NP1]
(
(X3::Y1)
(X1::Y2)
((X1 def) = +)
((X1 status) =c absolute)
((X1 num) = (X3 num))
((X1 gen) = (X3 gen))
(X0 = X1)
)
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Hebrew-to-English MT Prototype
• Initial prototype developed within a two month
intensive effort
• Accomplished:
–
–
–
–
–
–
–
Adapted available morphological analyzer
Constructed a preliminary translation lexicon
Translated and aligned Elicitation Corpus
Learned XFER rules
Developed (small) manual XFER grammar
System debugging and development
Evaluated performance on unseen test data using
automatic evaluation metrics
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Example Translation
• Input:
– ‫לאחר דיונים רבים החליטה הממשלה לערוך משאל עם בנושא הנסיגה‬
– After debates many decided the government to hold
referendum in issue the withdrawal
• Output:
– AFTER MANY DEBATES THE GOVERNMENT DECIDED
TO HOLD A REFERENDUM ON THE ISSUE OF THE
WITHDRAWAL
April 20, 2011
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Noun Phrases – Construct State
‫החלטת הנשיא הראשון‬
HXL@T
[HNSIA
HRA$WN]
decision.3SF-CS the-president.3SM the-first.3SM
THE DECISION OF THE FIRST PRESIDENT
‫החלטת הנשיא הראשונה‬
[HXL@T
HNSIA]
decision.3SF-CS the-president.3SM
HRA$WNH
the-first.3SF
THE FIRST DECISION OF THE PRESIDENT
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Noun Phrases - Possessives
‫הנשיא הכריז שהמשימה הראשונה שלו תהיה למצוא פתרון לסכסוך באזורנו‬
HNSIA
HKRIZ
$HM$IMH
HRA$WNH $LW
THIH
the-president announced that-the-task.3SF the-first.3SF of-him will.3SF
LMCWA PTRWN LSKSWK
to-find solution to-the-conflict
BAZWRNW
in-region-POSS.1P
Without transfer grammar:
THE PRESIDENT ANNOUNCED THAT THE TASK THE BEST OF HIM
WILL BE TO FIND SOLUTION TO THE CONFLICT IN REGION OUR
With transfer grammar:
THE PRESIDENT ANNOUNCED THAT HIS FIRST TASK WILL BE
TO FIND A SOLUTION TO THE CONFLICT IN OUR REGION
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Subject-Verb Inversion
‫אתמול הודיעה הממשלה שתערכנה בחירות בחודש הבא‬
ATMWL
HWDI&H
HMM$LH
yesterday announced.3SF the-government.3SF
$T&RKNH
BXIRWT
BXWD$
HBA
that-will-be-held.3PF elections.3PF in-the-month the-next
Without transfer grammar:
YESTERDAY ANNOUNCED THE GOVERNMENT THAT WILL RESPECT
OF THE FREEDOM OF THE MONTH THE NEXT
With transfer grammar:
YESTERDAY THE GOVERNMENT ANNOUNCED THAT ELECTIONS
WILL ASSUME IN THE NEXT MONTH
April 20, 2011
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Subject-Verb Inversion
‫לפני כמה שבועות הודיעה הנהלת המלון שהמלון יסגר בסוף השנה‬
LPNI
before
KMH $BW&WT HWDI&H
HNHLT
HMLWN
several weeks
announced.3SF management.3SF.CS the-hotel
$HMLWN
ISGR
BSWF
H$NH
that-the-hotel.3SM will-be-closed.3SM at-end.3SM.CS the-year
Without transfer grammar:
IN FRONT OF A FEW WEEKS ANNOUNCED ADMINISTRATION THE
HOTEL THAT THE HOTEL WILL CLOSE AT THE END THIS YEAR
With transfer grammar:
SEVERAL WEEKS AGO THE MANAGEMENT OF THE HOTEL ANNOUNCED
THAT THE HOTEL WILL CLOSE AT THE END OF THE YEAR
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Evaluation Results
• Test set of 62 sentences from Haaretz
newspaper, 2 reference translations
System
BLEU
NIST
P
R
METEOR
No Gram
0.0616 3.4109 0.4090
0.4427
0.3298
Learned
0.0774 3.5451 0.4189
0.4488
0.3478
Manual
0.1026 3.7789 0.4334
0.4474
0.3617
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Current and Future Work
• Issues specific to the Hebrew-to-English system:
– Coverage: further improvements in the translation lexicon
and morphological analyzer
– Manual Grammar development
– Acquiring/training of word-to-word translation probabilities
– Acquiring/training of a Hebrew language model at a postmorphology level that can help with disambiguation
• General Issues related to XFER framework:
–
–
–
–
Discriminative Language Modeling for MT
Effective models for assigning scores to transfer rules
Improved grammar learning
Merging/integration of manual and acquired grammars
April 20, 2011
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Conclusions
• Test case for the CMU XFER framework for
rapid MT prototyping
• Preliminary system was a two-month, three
person effort – we were quite happy with the
outcome
• Core concept of XFER + Decoding is very
powerful and promising for low-resource MT
• We experienced the main bottlenecks of
knowledge acquisition for MT: morphology,
translation lexicons, grammar...
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Mapudungun-to-Spanish
Example
English
I didn’t see Maria
Mapudungun
pelafiñ Maria
Spanish
No vi a María
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Mapudungun-to-Spanish
Example
English
I didn’t see Maria
Mapudungun
pelafiñ Maria
pe -la -fi
-ñ
Maria
see -neg -3.obj -1.subj.indicative Maria
Spanish
No vi a María
No vi
a María
neg see.1.subj.past.indicative acc Maria
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pe-la-fi-ñ Maria
V
pe
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61
pe-la-fi-ñ Maria
V
pe
VSuff
Negation = +
la
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62
pe-la-fi-ñ Maria
V
pe
VSuffG
Pass all features up
VSuff
la
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63
pe-la-fi-ñ Maria
V
pe
VSuffG VSuff
object person = 3
VSuff
fi
la
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pe-la-fi-ñ Maria
V
pe
VSuffG
VSuffG VSuff
VSuff
Pass all features up
from both children
fi
la
April 20, 2011
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pe-la-fi-ñ Maria
V
pe
VSuffG
VSuff
VSuffG VSuff
ñ
VSuff
person = 1
number = sg
mood = ind
fi
la
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pe-la-fi-ñ Maria
VSuffG
V
pe
VSuffG
VSuff
VSuffG VSuff
ñ
VSuff
Pass all features up
from both children
fi
la
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pe-la-fi-ñ Maria
Pass all features up
from both children
V
V
pe
Check that:
1) negation = +
VSuffG
2) tense is undefined
VSuffG
VSuff
VSuffG VSuff
VSuff
ñ
fi
la
April 20, 2011
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pe-la-fi-ñ Maria
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
person = 3
number = sg
human = +
fi
la
April 20, 2011
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pe-la-fi-ñ Maria
S
Pass features up
from V
Check that NP is
human = +
VP
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
fi
la
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Transfer to Spanish: Top-Down
S
S
VP
VP
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
fi
la
April 20, 2011
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Transfer to Spanish: Top-Down
Pass all features
to Spanish side
S
S
VP
VP
NP
V
pe
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
“a”
NP
N
VSuffG
V
V
fi
la
April 20, 2011
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Transfer to Spanish: Top-Down
S
VP
NP
V
pe
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
VP
V
“a”
NP
N
VSuffG
V
S
Pass all
features
down
fi
la
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Transfer to Spanish: Top-Down
S
S
VP
VP
NP
V
pe
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
“a”
NP
N
VSuffG
V
V
Pass
object
features
down
fi
la
April 20, 2011
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Transfer to Spanish: Top-Down
S
S
VP
VP
NP
V
pe
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
“a”
NP
N
VSuffG
V
V
fi
Accusative
marker on
objects is
introduced
because
human = +
la
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Transfer to Spanish: Top-Down
S
S
VP VP::VP [VBar NP] -> [VBar "a" NP] VP
( (X1::Y1)
NP
V
V
“a”
NP
(X2::Y3)
pe
N = (*NOT* personal))
((X2 type)
((X2 human) =c +)
VSuff
(X0 = N
X1)
((X0 object) = X2)
VSuffG
V
VSuffG
VSuffG VSuff
VSuff
la
April 20, 2011
fi
ñ (Y0Maria
= 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)))
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Transfer to Spanish: Top-Down
S
VP
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
S
Pass person, number,
andVP
mood features to
Spanish Verb
“a”
NP
V
“no” V
Assign tense = past
fi
la
April 20, 2011
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Transfer to Spanish: Top-Down
S
S
VP
VP
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
fi
“a”
V
NP
“no” V
Introduced because
negation = +
la
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Transfer to Spanish: Top-Down
S
S
VP
VP
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
“a”
V
“no”
NP
V
ver
fi
la
April 20, 2011
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Transfer to Spanish: Top-Down
S
S
VP
VP
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
“a”
V
“no”
V
ver
vi
fi
la
April 20, 2011
NP
11-731 Machine Translation
person = 1
number = sg
mood = indicative
tense = past
80
Transfer to Spanish: Top-Down
S
S
Pass features over to
VP Spanish side
VP
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
“a”
V
“no”
NP
V
N
vi
N
María
fi
la
April 20, 2011
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I Didn’t see Maria
S
S
VP
VP
NP
V
pe
N
VSuffG
V
VSuffG
VSuff
N
VSuffG VSuff
ñ
Maria
VSuff
“a”
V
“no”
NP
V
N
vi
N
María
fi
la
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