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
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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
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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
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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
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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)))
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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
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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')) ) ) ) ")
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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)
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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')))))))>
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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
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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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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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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--|--$WRH---|
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Morphology ExampleY0: ((SPANSTART 0) Y1: ((SPANSTART 0) Y2: ((SPANSTART 1) (SPANEND 4) (SPANEND 2) (SPANEND 3) (LEX B$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))
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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 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 $MLWT H ADWMWT;;TL: THE RED DRESSES
NP1::NP1 [NP1 "H" ADJ] -> [ADJ NP1]((X3::Y1)(X1::Y2)((X1 def) = +)((X1 status) =c absolute)((X1 num) = (X3 num))((X1 gen) = (X3 gen))(X0 = X1))
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Hebrew-to-English MT Prototype
• Initial prototype developed within a two month intensive effort
• Accomplished:– Adapted available morphological analyzer– Constructed a preliminary translation lexicon– Translated and aligned Elicitation Corpus– Learned XFER rules– Developed (small) manual XFER grammar– System debugging and development– Evaluated performance on unseen test data using
automatic evaluation metrics
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Example Translation
• Input: – הנסיגה בנושא עם משאל לערוך הממשלה החליטה רבים דיונים לאחר– After debates many decided the government to hold
referendum in issue the withdrawal
• Output: – AFTER MANY DEBATES THE GOVERNMENT DECIDED
TO HOLD A REFERENDUM ON THE ISSUE OF THE WITHDRAWAL
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Noun Phrases – Construct State
HXL@T [HNSIA HRA$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
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Noun Phrases - Possessives
HNSIA HKRIZ $HM$IMH HRA$WNH $LW THIHthe-president announced that-the-task.3SF the-first.3SF of-him will.3SF
LMCWA PTRWN LSKSWK BAZWRNWto-find solution to-the-conflict in-region-POSS.1P
נו תהיה למצוא פתרון לסכסוך באזורשלוהנשיא הכריז שהמשימה הראשונה
Without transfer grammar:THE PRESIDENT ANNOUNCED THAT THE TASK THE BEST OF HIM WILL BE TO FIND SOLUTION TO THE CONFLICT IN REGION OUR
With transfer grammar:THE PRESIDENT ANNOUNCED THAT HIS FIRST TASK WILL BE TO FIND A SOLUTION TO THE CONFLICT IN OUR REGION
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Subject-Verb Inversion
ATMWL HWDI&H HMM$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
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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
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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 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
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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 MT
• We experienced the main bottlenecks of knowledge acquisition for MT: morphology, translation lexicons, grammar...
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