rapid prototyping of a transfer-based hebrew-to-english machine translation system alon lavie...

Rapid Prototyping of a Transfer-based Hebrew-to-English

Machine Translation System

Alon LavieLanguage Technologies Institute

Carnegie Mellon University

Joint work with:Shuly Wintner, Danny Shacham, Nurit Melnik, Yuval Krymolowski - University of HaifaErik Peterson – Carnegie Mellon University

June 20, 2007 ISCOL/BISFAI-2007 2

Outline

• Context of this Work• CMU Statistical Transfer MT Framework• Hebrew and its Challenges for MT• Hebrew-to-English System• Morphological Analysis and Generation• MT Resources: lexicon and grammar• Translation Examples• Performance Evaluation• Conclusions, Current and Future Work


Current State-of-the-art in Machine Translation

• MT underwent a major paradigm shift over the past 15 years:– From manually crafted rule-based systems with manually

designed knowledge resources– To search-based approaches founded on automatic

extraction of translation models/units from large sentence-parallel corpora

• Current Dominant Approach: Phrase-based Statistical MT:– Extract and statistically model large volumes of phrase-to-

phrase correspondences from automatically word-aligned parallel corpora

– “Decode” new input by searching for the most likely sequence of phrase matches, using a statistical Language Model for the target language


Current State-of-the-art in Machine Translation

• Phrase-based MT State-of-the-art:– Requires minimally several million words of parallel

text for adequate training– Limited to language-pairs for which such data exists:

major European languages, Chinese, Japanese, a few others…

– Linguistically shallow and highly lexicalized models result in weak generalization

– Best performance levels (BLEU=~0.6) on Arabic-to-English provide understandable but often still somewhat disfluent translations

– Ill suited for Hebrew and most of the world’s minor languages


CMU’s Statistical-Transfer (XFER) Approach

• Framework: Statistical search-based approach with syntactic translation transfer rules that can be acquired from data but also developed and extended by experts

• 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

• XFER + Decoder:– XFER engine produces a lattice of possible transferred

structures at all levels– Decoder searches and selects the best scoring combination

• Rule Refinement: refine the acquired rules via a process of interaction with bilingual informants

• Word and Phrase bilingual lexicon acquisition

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)))


The Transfer Engine

• Main algorithm: chart-style bottom-up integrated parsing+transfer with beam pruning– Seeded by word-to-word translations– Driven by transfer rules– Generates a lattice of transferred translation segments at

all levels• 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


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• Simple Stack Decoder, similar in principle to simple

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

components, with weights trained using MERT• Scoring components:

– Statistical Language Model– Fragmentation: how many arcs to cover the entire

translation?– Length Penalty– Rule Scores– Lexical Probabilities (not fully integrated)


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')))))))>


XFER MT Prototypes • General XFER framework under development for past

five years• Prototype systems so far:

– German-to-English– Dutch-to-English– Chinese-to-English– Hindi-to-English– Hebrew-to-English

• In progress or planned:– Mapudungun-to-Spanish– Quechua-to-Spanish– Brazilian Portuguese-to-English– Native-Brazilian languages to Brazilian Portuguese– Hebrew-to-Arabic


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


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


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 באזורשלוהנשיא 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 MT

• We experienced the main bottlenecks of knowledge acquisition for MT: morphology, translation lexicons, grammar...


Questions?

rapid prototyping of a transfer-based hebrew-to-english machine translation system alon lavie...

Documents

x1 translation lexicon

x1 status

wrh line x1

machine translation

transferbased hebrew

x3 num x1 gen

statistical mt

mt hebrew