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Statistical XFER: Hybrid Statistical Rule-based Machine Translation

Statistical XFER: Hybrid Statistical Rule-based Machine Translation. Alon Lavie Language Technologies Institute Carnegie Mellon University Joint work with:

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Statistical XFER: Hybrid Statistical Rule-based Machine Translation

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  1. 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

  2. Outline • Background and Rationale • Stat-XFER Framework Overview • Elicitation • Learning Transfer Rules • Automatic Rule Refinement • Example Prototypes • Major Research Challenges Statistical XFER MT

  3. Progression of MT • Started with rule-based systems • Very large expert human effort to construct language-specific 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? Statistical XFER MT

  4. 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 Statistical XFER MT

  5. 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 resource-poor language scenarios Statistical XFER MT

  6. Stat-XFER MT Approach Semantic Analysis Sentence Planning Interlingua Syntactic Parsing Transfer Rules Text Generation Statistical-XFER Source (e.g. Quechua) Target (e.g. English) Direct: SMT, EBMT Statistical XFER MT

  7. Source Input בשורה הבאה Preprocessing Morphology Transfer Rules Language Model + Additional Features {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)) Transfer Engine Translation Lexicon Decoder N::N |: ["$WR"] -> ["BULL"] ((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "BULL")) N::N |: ["$WRH"] -> ["LINE"] ((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "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) English Output in the next line Statistical XFER MT

  8. Type information Part-of-speech/constituent information Alignments x-side constraints y-side constraints xy-constraints, e.g. ((Y1 AGR) = (X1 AGR)) Transfer Rule Formalism ;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)) ) Statistical XFER MT

  9. Value constraints Agreement constraints Transfer Rule Formalism (II) ;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)) ) Statistical XFER MT

  10. 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 Statistical XFER MT

  11. Hebrew 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) ) Statistical XFER MT

  12. The XFER 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” 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 Statistical XFER MT

  13. Source-language Confusion Network Hebrew Example • Input word: B$WRH 0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---| Statistical XFER MT

  14. 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')) ) ) ) ") Statistical XFER MT

  15. 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 Statistical XFER MT

  16. XFER Lattice Decoder 0 0 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')))))))> Statistical XFER MT

  17. 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 Statistical XFER MT

  18. Elicitation Tool:English-Chinese Example Statistical XFER MT

  19. Elicitation Tool:English-Chinese Example Statistical XFER MT

  20. Elicitation Tool:English-Hindi Example Statistical XFER MT

  21. Elicitation Tool:English-Arabic Example Statistical XFER MT

  22. Elicitation Tool:Spanish-Mapudungun Example Statistical XFER MT

  23. 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 Statistical XFER MT

  24. Rule Learning - Overview • Goal: Acquire Syntactic Transfer Rules • Use available knowledge from the source side (grammatical structure) • Three steps: • Flat Seed Generation: first guesses at transfer rules; flat syntactic structure • Compositionality Learning:use previously learned rules to learn hierarchical structure • Constraint Learning: refine rules by learning appropriate feature constraints Statistical XFER MT

  25. Flat Seed Rule Generation Statistical XFER MT

  26. Compositionality Learning Statistical XFER MT

  27. Constraint Learning Statistical XFER MT

  28. 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] Statistical XFER MT

  29. 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 • In progress or planned: • Brazilian Portuguese-to-English • Native-Brazilian languages to Brazilian Portuguese • Hebrew-to-Arabic • Iñupiaq-to-English • Urdu-to-English • Turkish-to-English Statistical XFER MT

  30. 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 transfer-rules from parallel data Statistical XFER MT

  31. Translation Example • REFERENCE:When responding to whether it is possible to extend Russian fleet's stationing deadline at the Crimean peninsula, Yanukovych replied, "Without a doubt. • 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 . Statistical XFER MT

  32. 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 Statistical XFER MT

  33. 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 word-alignment 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 Statistical XFER MT

  34. 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 Statistical XFER MT

  35. 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 Statistical XFER MT

  36. Major Research Directions • Building Elicitation Corpora: • Feature Detection • Corpus Navigation • Automatic Rule Refinement • Translation for highly polysynthetic languages such as Mapudungun and Iñupiaq Statistical XFER MT

  37. Questions? Statistical XFER MT

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