Statistical XFER: Hybrid Statistical Rule-based Machine Translation Alon Lavie Language Technologies Institute Carnegie Mellon University Joint work with: Jaime Carbonell, Lori Levin, Bob Frederking, Erik Peterson, Christian Monson, Vamshi Ambati, Greg Hanneman, Kathrin Probst, Ariadna Font-Llitjos, Alison Alvarez, Roberto Aranovich Outline • • • • • • • Background and Rationale Stat-XFER Framework Overview Elicitation Learning Transfer Rules Automatic Rule Refinement Example Prototypes Major Research Challenges Aug 29, 2007 Statistical XFER MT 2 Progression of MT • Started with rule-based systems – Very large expert human effort to construct languagespecific resources (grammars, lexicons) – High-quality MT extremely expensive only for handful of language pairs • Along came EBMT and then Statistical MT… – Replaced human effort with extremely large volumes of parallel text data – Less expensive, but still only feasible for a small number of language pairs – We “traded” human labor with data • Where does this take us in 5-10 years? – Large parallel corpora for maybe 25-50 language pairs • What about all the other languages? • Is all this data (with very shallow representation of language structure) really necessary? • Can we build MT approaches that learn deeper levels of language structure and how they map from one language to another? Aug 29, 2007 Statistical XFER MT 3 Rule-based vs. Statistical MT • Traditional Rule-based MT: – Expressive and linguistically-rich formalisms capable of describing complex mappings between the two languages – Accurate “clean” resources – Everything constructed manually by experts – Main challenge: obtaining broad coverage • Phrase-based Statistical MT: – Learn word and phrase correspondences automatically from large volumes of parallel data – Search-based “decoding” framework: • Models propose many alternative translations • Effective search algorithms find the “best” translation – Main challenge: obtaining high translation accuracy Aug 29, 2007 Statistical XFER MT 4 Main Principles of Stat-XFER • 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 basic phrase correspondences (i.e. base NPs) can be automatically acquired from parallel text when available – 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 Aug 29, 2007 Statistical XFER MT 5 Stat-XFER MT Approach Interlingua Semantic Analysis Syntactic Parsing Sentence Planning Transfer Rules Text Generation Statistical-XFER Source (e.g. Quechua) Aug 29, 2007 Direct: SMT, EBMT Statistical XFER MT Target (e.g. English) 6 Source 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 Transfer Engine Language Model + Additional Features 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")) Aug 29, 2007 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) Statistical XFER MT (0 4 "IN THE NEXT LINE" @PP) English Output in the next line 7 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)) Aug 29, 2007 NP::NP ( (X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2) ((Y1 DEF) = *DEF) ((Y3 DEF) = *DEF) ((Y2 AGR) = *3-SING) ((Y2 GENDER) = (Y4 GENDER)) ) Statistical XFER MT 8 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 Aug 29, 2007 [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)) ) Statistical XFER MT 9 Hebrew Manual Transfer Grammar (human-developed) • Initially developed in a couple of days, with some later revisions by a CL post-doc • 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 Aug 29, 2007 Statistical XFER MT 10 Hebrew Transfer Grammar Example Rules {NP1,2} ;;SL: $MLH ADWMH ;;TL: A RED DRESS {NP1,3} ;;SL: H $MLWT H ADWMWT ;;TL: THE RED DRESSES NP1::NP1 [NP1 ADJ] -> [ADJ NP1] ( (X2::Y1) (X1::Y2) ((X1 def) = -) ((X1 status) =c absolute) ((X1 num) = (X2 num)) ((X1 gen) = (X2 gen)) (X0 = X1) ) NP1::NP1 [NP1 "H" ADJ] -> [ADJ NP1] ( (X3::Y1) (X1::Y2) ((X1 def) = +) ((X1 status) =c absolute) ((X1 num) = (X3 num)) ((X1 gen) = (X3 gen)) (X0 = X1) ) Aug 29, 2007 Statistical XFER MT 11 The XFER Engine • Input: source-language input sentence, or sourcelanguage confusion network • Output: lattice representing collection of translation fragments at all levels supported by transfer rules • Basic Algorithm: “bottom-up” integrated “parsingtransfer-generation” guided by the transfer rules – Start with translations of individual words and phrases from translation lexicon – Create translations of larger constituents by applying applicable transfer rules to previously created lattice entries – Beam-search controls the exponential combinatorics of the search-space, using multiple scoring features Aug 29, 2007 Statistical XFER MT 12 Source-language Confusion Network Hebrew Example • Input word: B$WRH 0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---| Aug 29, 2007 Statistical XFER MT 13 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')) ) ) ) ") Aug 29, 2007 Statistical XFER MT 14 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 Aug 29, 2007 Statistical XFER MT 15 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')))))))> Aug 29, 2007 Statistical XFER MT 16 Data Elicitation for Languages with Limited Resources • Rationale: – Large volumes of parallel text not available create a small maximally-diverse parallel corpus that directly supports the learning task – Bilingual native informant(s) can translate and align a small pre-designed elicitation corpus, using elicitation tool – Elicitation corpus designed to be typologically and structurally comprehensive and compositional – Transfer-rule engine and new learning approach support acquisition of generalized transfer-rules from the data Aug 29, 2007 Statistical XFER MT 17 Elicitation Tool: English-Chinese Example Aug 29, 2007 Statistical XFER MT 18 Elicitation Tool: English-Chinese Example Aug 29, 2007 Statistical XFER MT 19 Elicitation Tool: English-Hindi Example Aug 29, 2007 Statistical XFER MT 20 Elicitation Tool: English-Arabic Example Aug 29, 2007 Statistical XFER MT 21 Elicitation Tool: Spanish-Mapudungun Example Aug 29, 2007 Statistical XFER MT 22 Designing Elicitation Corpora • Goal: Create a small representative parallel corpus that contains examples of the most important translation correspondences and divergences between the two languages • Method: – Elicit translations and word alignments for a broad diversity of linguistic phenomena and constructions • Current Elicitation Corpus: ~3100 sentences and phrases, constructed based on a broad feature-based specification • Open Research Issues: – Feature Detection: discover what features exist in the language and where/how they are marked • Example: does the language mark gender of nouns? How and where are these marked? – Dynamic corpus navigation based on feature detection: no need to elicit for combinations involving non-existent features Aug 29, 2007 Statistical XFER MT 23 Rule Learning - Overview • Goal: Acquire Syntactic Transfer Rules • Use available knowledge from the source side (grammatical structure) • Three steps: 1. Flat Seed Generation: first guesses at transfer rules; flat syntactic structure 2. Compositionality Learning: use previously learned rules to learn hierarchical structure 3. Constraint Learning: refine rules by learning appropriate feature constraints Aug 29, 2007 Statistical XFER MT 24 Flat Seed Rule Generation Learning Example: NP Eng: the big apple Heb: ha-tapuax ha-gadol Generated Seed Rule: NP::NP [ART ADJ N] [ART N ART ADJ] ((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2)) Aug 29, 2007 Statistical XFER MT 25 Compositionality Learning Initial Flat Rules: S::S [ART ADJ N V ART N] [ART N ART ADJ V P ART N] ((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2) (X4::Y5) (X5::Y7) (X6::Y8)) NP::NP [ART ADJ N] [ART N ART ADJ] ((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2)) NP::NP [ART N] [ART N] ((X1::Y1) (X2::Y2)) Generated Compositional Rule: S::S [NP V NP] [NP V P NP] ((X1::Y1) (X2::Y2) (X3::Y4)) Aug 29, 2007 Statistical XFER MT 26 Constraint Learning Input: Rules and their Example Sets S::S [NP V NP] [NP V P NP] ((X1::Y1) (X2::Y2) (X3::Y4)) {ex1,ex12,ex17,ex26} NP::NP [ART ADJ N] [ART N ART ADJ] {ex2,ex3,ex13} ((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2)) NP::NP [ART N] [ART N] ((X1::Y1) (X2::Y2)) {ex4,ex5,ex6,ex8,ex10,ex11} Output: Rules with Feature Constraints: S::S [NP V NP] [NP V P NP] ((X1::Y1) (X2::Y2) (X3::Y4) (X1 NUM = X2 NUM) (Y1 NUM = Y2 NUM) (X1 NUM = Y1 NUM)) Aug 29, 2007 Statistical XFER MT 27 Automated Rule Refinement • Bilingual informants can identify translation errors and pinpoint the errors • A sophisticated trace of the translation path can identify likely sources for the error and do “Blame Assignment” • Rule Refinement operators can be developed to modify the underlying translation grammar (and lexicon) based on characteristics of the error source: – Add or delete feature constraints from a rule – Bifurcate a rule into two rules (general and specific) – Add or correct lexical entries • See [Font-Llitjos, Carbonell & Lavie, 2005] Aug 29, 2007 Statistical XFER MT 28 Stat-XFER MT Prototypes • General Statistical XFER framework under development for past five years (funded by NSF and DARPA) • Prototype systems so far: – – – – – – Chinese-to-English Dutch-to-English French-to-English Hindi-to-English Hebrew-to-English Mapudungun-to-Spanish – – – – – – Brazilian Portuguese-to-English Native-Brazilian languages to Brazilian Portuguese Hebrew-to-Arabic Iñupiaq-to-English Urdu-to-English Turkish-to-English • In progress or planned: Aug 29, 2007 Statistical XFER MT 29 Chinese-English Stat-XFER System • Bilingual lexicon: over 1.1 million entries (multiple resources, incl. ADSO, Wikipedia, extracted base NPs) • Manual syntactic XFER grammar: 76 rules! (mostly NPs, a few PPs, and reordering of NPs/PPs within VPs) • Multiple overlapping Chinese word segmentations • English morphology generation • Uses CMU SMT-group’s Suffix-Array LM toolkit for LM • Current Performance (GALE dev-test): – NW: • XFER: 10.89(B)/0.4509(M) • Best (UMD): 15.58(B)/0.4769(M) – NG • XFER: 8.92(B)/0.4229(M) • Best (UMD): 12.96(B)/0.4455(M) • In Progress: – Automatic extraction of “clean” base NPs from parallel data – Automatic learning and extraction of high-quality transferrules from parallel data Aug 29, 2007 Statistical XFER MT 30 Translation Example • REFERENCE: When responding to whether it is possible • Stat-XFER (0.3989): In reply to whether the possibility to extend the Russian fleet stationed in Crimea Pen. left the deadline of the problem , Yanukovich replied : " of course . IBM-ylee (0.2203): In response to the possibility to extend the deadline for the presence in Crimea peninsula , the Queen Vic said : " of course . CMU-SMT (0.2067): In response to a possible extension of the fleet in the Crimean Peninsula stay on the issue , Yanukovych vetch replied : " of course . maryland-hiero (0.1878): In response to the possibility of extending the mandate of the Crimean peninsula in , replied: "of course. IBM-smt (0.1862): The answer is likely to be extended the Crimean peninsula of the presence of the problem, Yanukovych said: " Of course. CMU-syntax (0.1639): In response to the possibility of extension of the presence in the Crimean Peninsula , replied : " of course . • • • • • to extend Russian fleet's stationing deadline at the Crimean peninsula, Yanukovych replied, "Without a doubt. Aug 29, 2007 Statistical XFER MT 31 Major Research Directions • Automatic Transfer Rule Learning: – From manually word-aligned elicitation corpus – From large volumes of automatically word-aligned “wild” parallel data – In the absence of morphology or POS annotated lexica – Compositionality and generalization – Identifying “good” rules from “bad” rules – Effective models for rule scoring for • Decoding: using scores at runtime • Pruning the large collections of learned rules – Learning Unification Constraints Aug 29, 2007 Statistical XFER MT 32 Major Research Directions • Extraction of Base-NP translations from parallel data: – Base-NPs are extremely important “building blocks” for transfer-based MT systems • Frequent, often align 1-to-1, improve coverage • Correctly identifying them greatly helps automatic wordalignment of parallel sentences – Parsers (or NP-chunkers) available for both languages: Extract base-NPs independently on both sides and find their correspondences – Parsers (or NP-chunkers) available for only one language (i.e. English): Extract base-NPs on one side, and find reliable correspondences for them using word-alignment, frequency distributions, other features… • Promising preliminary results Aug 29, 2007 Statistical XFER MT 33 Major Research Directions • Algorithms for XFER and Decoding – Integration and optimization of multiple features into search-based XFER parser – Complexity and efficiency improvements (i.e. “Cube Pruning”) – Non-monotonicity issues (LM scores, unification constraints) and their consequences on search Aug 29, 2007 Statistical XFER MT 34 Major Research Directions • Discriminative Language Modeling for MT: – Current standard statistical LMs provide only weak discrimination between good and bad translation hypotheses – New Idea: Use “occurrence-based” statistics: • Extract instances of lexical, syntactic and semantic features from each translation hypothesis • Determine whether these instances have been “seen before” (at least once) in a large monolingual corpus – The Conjecture: more grammatical MT hypotheses are likely to contain higher proportions of feature instances that have been seen in a corpus of grammatical sentences. – Goals: • Find the set of features that provides the best discrimination between good and bad translations • Learn how to combine these into a LM-like function for scoring alternative MT hypotheses Aug 29, 2007 Statistical XFER MT 35 Major Research Directions • Building Elicitation Corpora: – Feature Detection – Corpus Navigation • Automatic Rule Refinement • Translation for highly polysynthetic languages such as Mapudungun and Iñupiaq Aug 29, 2007 Statistical XFER MT 36 Questions? Aug 29, 2007 Statistical XFER MT 37

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# Automatic Rule Learning for Resource Limited MT