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MT for Languages with Limited Resources

11-731Machine 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

April 20, 2011 11-731 Machine Translation 2

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)


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


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


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 resource-poor language scenarios


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


Stat-XFER FrameworkSourceInput

Preprocessing

Morphology

TransferEngine

TransferRules

BilingualLexicon

TranslationLattice

Second-StageDecoder

LanguageModel

WeightedFeatures

TargetOutput

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


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)))


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)))


Translation Lexicon: Hebrew-to-English Examples(Semi-manually-developed)

PRO::PRO |: ["ANI"] -> ["I"]((X1::Y1)((X0 per) = 1)((X0 num) = s)((X0 case) = nom))

PRO::PRO |: ["ATH"] -> ["you"]((X1::Y1)((X0 per) = 2)((X0 num) = s)((X0 gen) = m)((X0 case) = nom))

N::N |: ["$&H"] -> ["HOUR"]((X1::Y1)((X0 NUM) = s)((Y0 NUM) = s)((Y0 lex) = "HOUR"))

N::N |: ["$&H"] -> ["hours"]((X1::Y1)((Y0 NUM) = p)((X0 NUM) = p)((Y0 lex) = "HOUR"))


Translation Lexicon: French-to-English Examples

(Automatically-acquired)DET::DET |: [“le"] -> [“the"]((X1::Y1))

Prep::Prep |:[“dans”] -> [“in”]((X1::Y1))

N::N |: [“principes"] -> [“principles"]((X1::Y1))

N::N |: [“respect"] -> [“accordance"]((X1::Y1))

NP::NP |: [“le respect"] -> [“accordance"]()

PP::PP |: [“dans le respect"] -> [“in accordance"]()

PP::PP |: [“des principes"] -> [“with the principles"]()


Hebrew-English Transfer GrammarExample Rules

(Manually-developed)

{NP1,2};;SL: $MLH ADWMH;;TL: A RED DRESS

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))

{NP1,3};;SL: H $MLWT H ADWMWT;;TL: THE RED DRESSES

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))


French-English Transfer GrammarExample Rules

(Automatically-acquired)

{PP,24691};;SL: des principes;;TL: with the principles

PP::PP [“des” N] -> [“with the” N]((X1::Y1))

{PP,312};;SL: dans le respect des principes;;TL: in accordance with the principles

PP::PP [Prep NP] -> [Prep NP]((X1::Y1)(X2::Y2))


The Transfer Engine

• Input: source-language input sentence, or source-language confusion network

• Output: lattice representing collection of translation fragments at all levels supported by transfer rules

• Basic Algorithm: “bottom-up” integrated “parsing-transfer-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


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


Hebrew Example(From [Lavie et al., 2004])

• Input word: B$WRH

0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---|


Hebrew Example (From [Lavie et al., 2004])

Y0: ((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))


XFER Output Lattice(28 28 "AND" -5.6988 "W" "(CONJ,0 'AND')")(29 29 "SINCE" -8.20817 "MAZ " "(ADVP,0 (ADV,5 'SINCE')) ")(29 29 "SINCE THEN" -12.0165 "MAZ " "(ADVP,0 (ADV,6 'SINCE THEN')) ")(29 29 "EVER SINCE" -12.5564 "MAZ " "(ADVP,0 (ADV,4 'EVER SINCE')) ")(30 30 "WORKED" -10.9913 "&BD " "(VERB,0 (V,11 'WORKED')) ")(30 30 "FUNCTIONED" -16.0023 "&BD " "(VERB,0 (V,10 'FUNCTIONED')) ")(30 30 "WORSHIPPED" -17.3393 "&BD " "(VERB,0 (V,12 'WORSHIPPED')) ")(30 30 "SERVED" -11.5161 "&BD " "(VERB,0 (V,14 'SERVED')) ")(30 30 "SLAVE" -13.9523 "&BD " "(NP0,0 (N,34 'SLAVE')) ")(30 30 "BONDSMAN" -18.0325 "&BD " "(NP0,0 (N,36 'BONDSMAN')) ")(30 30 "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')) ) ) ) ")


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?


XFER Lattice Decoder0 0 ON THE FOURTH DAY THE LION ATE THE RABBIT TO A MORNING MEALOverall: -8.18323, Prob: -94.382, Rules: 0, Frag: 0.153846, Length: 0,

Words: 13,13235 < 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')))))))>


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


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


English-Chinese Example


English-Hindi Example


Spanish-Mapudungun Example


English-Arabic Example


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


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


The Structural Elicitation Corpus

Examples:

srcsent: in the foresttgtsent: B H I&Raligned: ((1,1),(2,2),(3,3))context: C-Structure:(<PP> (PREP in-1) (<NP> (DET the-2) (N forest-3)))

srcsent: stepstgtsent: MDRGWTaligned: ((1,1))context: C-Structure:(<NP> (N steps-1))

srcsent: the boy ate the appletgtsent: H ILD AKL AT H TPWXaligned: ((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 yeartgtsent: H $NH H RA$WNHaligned: ((1,1 3),(2,4),(3,2))context: C-Structure:(<NP> (DET the-1) (<ADJP> (ADJ first-2)) (N year-3))


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!!


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))


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))


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


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)


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)


Results on JHU Test Set

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


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


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


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”


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


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


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


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)


Morphology Example

• Input word: B$WRH

0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---|


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))


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)


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


Transfer GrammarExample Rules

{NP1,2};;SL: $MLH ADWMH;;TL: A RED DRESS

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))

{NP1,3};;SL: H $MLWT H ADWMWT;;TL: THE RED DRESSES

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))


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


Example Translation

• Input: – הנסיגה בנושא עם משאל לערוך הממשלה החליטה רבים דיונים לאחר– 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


Noun Phrases – Construct State

HXL@T [HNSIA HRA$WN]decision.3SF-CS the-president.3SM the-first.3SM

החלטת הנשיא הראשון

החלטת הנשיא הראשונה

[HXL@T HNSIA] HRA$WNHdecision.3SF-CS the-president.3SM the-first.3SF

THE DECISION OF THE FIRST PRESIDENT

THE FIRST DECISION OF THE PRESIDENT


Noun Phrases - Possessives

HNSIA HKRIZ $HM$IMH HRA$WNH $LW THIHthe-president announced that-the-task.3SF the-first.3SF of-him will.3SF

LMCWA PTRWN LSKSWK BAZWRNWto-find solution to-the-conflict in-region-POSS.1P

נו תהיה למצוא פתרון לסכסוך באזורשלו הנשיא הכריז שהמשימה הראשונה

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


Subject-Verb Inversion

ATMWL HWDI&H HMM$LHyesterday announced.3SF the-government.3SF

אתמול הודיעה הממשלה שתערכנה בחירות בחודש הבא

$T&RKNH BXIRWT BXWD$ HBAthat-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


Subject-Verb Inversion

LPNI KMH $BW&WT HWDI&H HNHLT HMLWNbefore several weeks announced.3SF management.3SF.CS the-hotel

לפני כמה שבועות הודיעה הנהלת המלון שהמלון יסגר בסוף השנה

$HMLWN ISGR BSWF H$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


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


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 post-

morphology 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


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...


Mapudungun-to-Spanish Example

Mapudungun

pelafiñ Maria

Spanish

No vi a María

English

I didn’t see Maria


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


V

pe

pe-la-fi-ñ Maria


V

pe

pe-la-fi-ñ Maria

VSuff

laNegation = +


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffGPass all features up


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fiobject person = 3


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffGPass all features up from both children


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

person = 1number = sgmood = ind


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

Pass all features up from both children

VSuffG


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


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

V NP

N

Maria

N person = 3number = sghuman = +


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


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


V

pe


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


V

pe


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


V

pe


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


V

pe


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 = +


V

pe


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)))


V

pe


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


V

pe


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 = +


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

V“no”

ver


V

pe


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


V

pe


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


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