1. Enabling MT for Languages with Limited Resources Alon Lavie Language Technologies Institute Carnegie Mellon University

2. October 5, 2004TMI 2004 Panel2 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 SMT… –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?

3. October 5, 2004TMI 2004 Panel3 Why Machine Translation for Languages with Limited Resources? We are in the age of information explosion –The internet+web+Google  anyone can get the information they want anytime… But what about the text in all those other languages? –How do they read all this English stuff? –How do we read all the stuff that they put online? MT for these languages would Enable: –Better government access to native indigenous and minority communities –Better minority and native community participation in information-rich activities (health care, education, government) without giving up their languages. –Civilian and military applications (disaster relief) –Language preservation

4. October 5, 2004TMI 2004 Panel4 The Roadmap to Learning-based MT Automatic acquisition of necessary language resources and knowledge using machine learning methodologies: –Learning morphology (analysis/generation) –Rapid acquisition of broad coverage word-to-word and phrase-to-phrase translation lexicons –Learning of syntactic structural mappings Tree-to-tree structure transformations [Knight et al], [Eisner], [Melamed] require parse trees for both languages Learning syntactic transfer rules with resources (grammar, parses) for just one of the two languages –Automatic rule refinement and/or post-editing Effective integration of acquired knowledge with statistical/distributional information

5. October 5, 2004TMI 2004 Panel5 CMU’s AVENUE Approach 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 –Learn from major language to minor language –Translate from minor language to major language XFER + Decoder: –XFER engine produces a lattice of all 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 Morphology Learning Word and Phrase bilingual lexicon acquisition

6. October 5, 2004TMI 2004 Panel6 AVENUE Architecture Learning Module Transfer Rules {PP,4894} ;;Score:0.0470 PP::PP [NP POSTP] -> [PREP NP] ((X2::Y1) (X1::Y2)) Translation Lexicon Run Time Transfer System Lattice Decoder English Language Model Word-to-Word Translation Probabilities Word-aligned elicited data

7. October 5, 2004TMI 2004 Panel7 Learning Transfer-Rules for Languages with Limited Resources Rationale: –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

8. October 5, 2004TMI 2004 Panel8 Transfer Rule Formalism Type information Part-of-speech/constituent information Alignments 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)) )

9. October 5, 2004TMI 2004 Panel9 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: use previously learned rules to add hierarchical structure 3.Seeded Version Space Learning: refine rules by learning appropriate feature constraints

10. October 5, 2004TMI 2004 Panel10 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))

11. October 5, 2004TMI 2004 Panel11 Compositionality 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))

12. October 5, 2004TMI 2004 Panel12 Seeded Version Space Learning Input: Rules and their Example Sets S::S [NP V NP]  [NP V P NP] {ex1,ex12,ex17,ex26} ((X1::Y1) (X2::Y2) (X3::Y4)) 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] {ex4,ex5,ex6,ex8,ex10,ex11} ((X1::Y1) (X2::Y2)) 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))

13. October 5, 2004TMI 2004 Panel13 AVENUE Prototypes General XFER framework under development for past two years Prototype systems so far: –German-to-English, Spanish-to-English –Hindi-to-English, Hebrew-to-English In progress or planned: –Mapudungun-to-Spanish –Quechua-to-Spanish –Arabic-to-English –Native-Brazilian language to Brazilian Portuguese

14. October 5, 2004TMI 2004 Panel14 Missing Science Monolingual learning tasks: –Learning morphology: morphemes and their meaning –Learning syntactic and semantic structures: grammar induction Bilingual Learning Tasks: –Automatic acquisition of word and phrase translation lexicons –Learning structural mappings (syntactic and semantic) Models that effectively combine learned symbolic knowledge with statistical information: new “decoders”

15. October 5, 2004TMI 2004 Panel15

16. October 5, 2004TMI 2004 Panel16 English-Chinese Example

17. October 5, 2004TMI 2004 Panel17 English-Hindi Example

18. October 5, 2004TMI 2004 Panel18 Spanish-Mapudungun Example

19. October 5, 2004TMI 2004 Panel19 English-Arabic Example

20. October 5, 2004TMI 2004 Panel20 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)) )

21. October 5, 2004TMI 2004 Panel21 AVENUE Partners LanguageCountryInstitutions Mapudungun (in place) Chile Universidad de la Frontera, Institute for Indigenous Studies, Ministry of Education Quechua (discussion) Peru Ministry of Education Aymara (discussion) Bolivia, Peru Ministry of Education

22. October 5, 2004TMI 2004 Panel22 The Transfer Engine Analysis Source text is parsed into its grammatical structure. Determines transfer application ordering. Example: 他 看 书。 (he read book) S NP VP N V NP 他 看 书 Transfer A target language tree is created by reordering, insertion, and deletion. S NP VP N V NP he read DET N a book Article “a” is inserted into object NP. Source words translated with transfer lexicon. Generation Target language constraints are checked and final translation produced. E.g. “reads” is chosen over “read” to agree with “he”. Final translation: “He reads a book”

23. October 5, 2004TMI 2004 Panel23 Seeded VSL: Some Open Issues Three types of constraints: –X-side constrain applicability of rule –Y-side assist in generation –X-Y transfer features from SL to TL Which of the three types improves translation performance? –Use rules without features to populate lattice, decoder will select the best translation… –Learn only X-Y constraints, based on list of universal projecting features Other notions of version-spaces of feature constraints: –Current feature learning is specific to rules that have identical transfer components –Important issue during transfer is to disambiguate among rules that have same SL side but different TL side – can we learn effective constraints for this?

24. October 5, 2004TMI 2004 Panel24 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:0.0470 PP::PP [NP POSTP] -> [PREP NP] ( (X2::Y1) (X1::Y2) )

25. October 5, 2004TMI 2004 Panel25 XFER MT for Hebrew-to-English Two month intensive effort to apply our XFER approach to the development of a Hebrew-to-English MT system Challenges: –No large parallel corpus –Limited coverage translation lexicon –Rich Morphology: incomplete analyzer available Accomplished: –Collected available resources, establish methodology for processing Hebrew input –Translated and aligned Elicitation Corpus –Learned XFER rules –Developed (small) manual XFER grammar as a point of comparison –System debugging and development –Evaluated performance on unseen test data using automatic evaluation metrics

26. October 5, 2004TMI 2004 Panel26 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

27. October 5, 2004TMI 2004 Panel27 Morphology Example Input word: B$WRH 0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---|

28. October 5, 2004TMI 2004 Panel28 Morphology Example 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))

29. October 5, 2004TMI 2004 Panel29 Sample Output (dev-data) maxwell anurpung comes from ghana for israel four years ago and since worked in cleaning in hotels in eilat a few weeks ago announced if management club hotel that for him to leave israel according to the government instructions and immigration police in a letter in broken english which spread among the foreign workers thanks to them hotel for their hard work and announced that will purchase for hm flight tickets for their countries from their money

30. October 5, 2004TMI 2004 Panel30 Evaluation Results Test set of 62 sentences from Haaretz newspaper, 2 reference translations SystemBLEUNISTPRMETEOR No Gram0.06163.41090.40900.44270.3298 Learned0.07743.54510.41890.44880.3478 Manual0.10263.77890.43340.44740.3617

31. October 5, 2004TMI 2004 Panel31 Future Directions Continued work on automatic rule learning (especially Seeded Version Space Learning) –Use Hebrew and Hindi systems as test platforms for experimenting with advanced learning research Rule Refinement via interaction with bilingual speakers Developing a well-founded model for assigning scores (probabilities) to transfer rules Redesigning and improving decoder to better fit the specific characteristics of the XFER model Improved leveraging from manual grammar resources MEMT with improved –Combination of output from different translation engines with different confidence scores – strong decoding capabilities

32. October 5, 2004TMI 2004 Panel32 Flat Seed Generation Create a transfer rule that is specific to the sentence pair, but abstracted to the POS level. No syntactic structure. ElementSource SL POS sequencef-structure TL POS sequenceTL dictionary, aligned SL words Type informationcorpus, same on SL and TL Alignmentsinformant x-side constraintsf-structure y-side constraintsTL dictionary, aligned SL words (list of projecting features)

33. October 5, 2004TMI 2004 Panel33 Compositionality - Overview Traverse the c-structure of the English sentence, add compositional structure for translatable chunks Adjust constituent sequences, alignments Remove unnecessary constraints, i.e. those that are contained in the lower- level rule

34. October 5, 2004TMI 2004 Panel34 Seeded Version Space Learning: Overview Goal: add appropriate feature constraints to the acquired rules Methodology: –Preserve general structural transfer –Learn specific feature constraints from example set Seed rules are grouped into clusters of similar transfer structure (type, constituent sequences, alignments) Each cluster forms a version space: a partially ordered hypothesis space with a specific and a general boundary The seed rules in a group form the specific boundary of a version space The general boundary is the (implicit) transfer rule with the same type, constituent sequences, and alignments, but no feature constraints

35. October 5, 2004TMI 2004 Panel35 Seeded Version Space Learning: Generalization The partial order of the version space: Definition: A transfer rule tr 1 is strictly more general than another transfer rule tr 2 if all f- structures that are satisfied by tr 2 are also satisfied by tr 1. Generalize rules by merging them: –Deletion of constraint –Raising two value constraints to an agreement constraint, e.g. ((x1 num) = *pl), ((x3 num) = *pl)  ((x1 num) = (x3 num))

36. October 5, 2004TMI 2004 Panel36 Seeded Version Space Learning NP v det nNP VP … 1.Group seed rules into version spaces as above. 2.Make use of partial order of rules in version space. Partial order is defined via the f-structures satisfying the constraints. 3.Generalize in the space by repeated merging of rules: 1.Deletion of constraint 2.Moving value constraints to agreement constraints, e.g. ((x1 num) = *pl), ((x3 num) = *pl)  ((x1 num) = (x3 num) 4. Check translation power of generalized rules against sentence pairs

37. October 5, 2004TMI 2004 Panel37 Seeded Version Space Learning: The Search The Seeded Version Space algorithm itself is the repeated generalization of rules by merging A merge is successful if the set of sentences that can correctly be translated with the merged rule is a superset of the union of sets that can be translated with the unmerged rules, i.e. check power of rule Merge until no more successful merges

38. October 5, 2004TMI 2004 Panel38 Conclusions Transfer rules (both manual and learned) offer significant contributions that can complement existing data-driven approaches –Also in medium and large data settings? Initial steps to development of a statistically grounded transfer-based MT system with: –Rules that are scored based on a well-founded probability model –Strong and effective decoding that incorporates the most advanced techniques used in SMT decoding Working from the “opposite” end of research on incorporating models of syntax into “standard” SMT systems [Knight et al] Our direction makes sense in the limited data scenario

39. October 5, 2004TMI 2004 Panel39 AVENUE Architecture User Learning Module Elicitation Process Transfer Rule Learning Transfer Rules Run-Time Module SL Input SL Parser Transfer Engine TL Generator TL Output Decoder Morphology Pre-proc

40. October 5, 2004TMI 2004 Panel40 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 new learning approach support acquisition of generalized transfer-rules from the data

41. October 5, 2004TMI 2004 Panel41 The 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

42. October 5, 2004TMI 2004 Panel42 The Transfer Engine Analysis Source text is parsed into its grammatical structure. Determines transfer application ordering. Example: 他 看 书。 (he read book) S NP VP N V NP 他 看 书 Transfer A target language tree is created by reordering, insertion, and deletion. S NP VP N V NP he read DET N a book Article “a” is inserted into object NP. Source words translated with transfer lexicon. Generation Target language constraints are checked and final translation produced. E.g. “reads” is chosen over “read” to agree with “he”. Final translation: “He reads a book”

43. October 5, 2004TMI 2004 Panel43 Transfer Rule Formalism Type information Part-of-speech/constituent information Alignments x-side constraints y-side constraints xy-constraints, e.g. ((Y1 AGR) = (X1 AGR)) ; SL: the man, TL: der Mann NP::NP [DET N] -> [DET N] ( (X1::Y1) (X2::Y2) ((X1 AGR) = *3-SING) ((X1 DEF = *DEF) ((X2 AGR) = *3-SING) ((X2 COUNT) = +) ((Y1 AGR) = *3-SING) ((Y1 DEF) = *DEF) ((Y2 AGR) = *3-SING) ((Y2 GENDER) = (Y1 GENDER)) )

44. October 5, 2004TMI 2004 Panel44 Transfer Rule Formalism (II) Value constraints Agreement constraints ;SL: the man, TL: der Mann NP::NP [DET N] -> [DET N] ( (X1::Y1) (X2::Y2) ((X1 AGR) = *3-SING) ((X1 DEF = *DEF) ((X2 AGR) = *3-SING) ((X2 COUNT) = +) ((Y1 AGR) = *3-SING) ((Y1 DEF) = *DEF) ((Y2 AGR) = *3-SING) ((Y2 GENDER) = (Y1 GENDER)) )

45. October 5, 2004TMI 2004 Panel45 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: use previously learned rules to add hierarchical structure 3.Seeded Version Space Learning: refine rules by generalizing with validation (learn appropriate feature constraints)

46. October 5, 2004TMI 2004 Panel46 Examples of Learned Rules (I) {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:0.0470 PP::PP [NP POSTP] -> [PREP NP] ( (X2::Y1) (X1::Y2) )

47. October 5, 2004TMI 2004 Panel47 A Limited Data Scenario for Hindi-to-English 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!!

48. October 5, 2004TMI 2004 Panel48 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)

49. October 5, 2004TMI 2004 Panel49 Manual Transfer Rules: 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) )

50. October 5, 2004TMI 2004 Panel50 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

51. October 5, 2004TMI 2004 Panel51 Adding a “Strong” Decoder XFER system produces a full lattice Edges are scored using word-to-word translation probabilities, trained from the limited bilingual data Decoder uses an English LM (70m words) Decoder can also reorder words or phrases (up to 4 positions ahead) For XFER (strong), ONLY edges from basic XFER system are used!

52. October 5, 2004TMI 2004 Panel52 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)

53. October 5, 2004TMI 2004 Panel53 Results on JHU Test Set (very miserly training data) SystemBLEUM-BLEUNIST EBMT0.0580.1654.22 SMT0.0930.1914.64 XFER (naïve) man grammar 0.0550.1774.46 XFER (strong) no grammar 0.1090.2245.29 XFER (strong) learned grammar 0.1160.2315.37 XFER (strong) man grammar 0.1350.2435.59 XFER+SMT0.1360.2435.65

54. October 5, 2004TMI 2004 Panel54 Effect of Reordering in the Decoder

55. October 5, 2004TMI 2004 Panel55 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 currently somewhat better than automatically learned grammar –Learned rules did not yet use version-space learning –Large room for improvement on learning rules –Importance of effective well-founded scoring of learned rules

56. October 5, 2004TMI 2004 Panel56 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”

57. October 5, 2004TMI 2004 Panel57 Conclusions Transfer rules (both manual and learned) offer significant contributions that can complement existing data-driven approaches –Also in medium and large data settings? Initial steps to development of a statistically grounded transfer-based MT system with: –Rules that are scored based on a well-founded probability model –Strong and effective decoding that incorporates the most advanced techniques used in SMT decoding Working from the “opposite” end of research on incorporating models of syntax into “standard” SMT systems [Knight et al] Our direction makes sense in the limited data scenario

58. October 5, 2004TMI 2004 Panel58 Future Directions Continued work on automatic rule learning (especially Seeded Version Space Learning) Improved leveraging from manual grammar resources, interaction with bilingual speakers Developing a well-founded model for assigning scores (probabilities) to transfer rules Improving the strong decoder to better fit the specific characteristics of the XFER model MEMT with improved –Combination of output from different translation engines with different scorings – strong decoding capabilities

59. October 5, 2004TMI 2004 Panel59 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; no syntactic structure 2.Compositionality: use previously learned rules to add structure 3.Seeded Version Space Learning: refine rules by generalizing with validation

60. October 5, 2004TMI 2004 Panel60 Flat Seed Generation Create a transfer rule that is specific to the sentence pair, but abstracted to the POS level. No syntactic structure. ElementSource SL POS sequencef-structure TL POS sequenceTL dictionary, aligned SL words Type informationcorpus, same on SL and TL Alignmentsinformant x-side constraintsf-structure y-side constraintsTL dictionary, aligned SL words (list of projecting features)

61. October 5, 2004TMI 2004 Panel61 Flat Seed Generation - Example The highly qualified applicant did not accept the offer. Der äußerst qualifizierte Bewerber nahm das Angebot nicht an. ((1,1),(2,2),(3,3),(4,4),(6,8),(7,5),(7,9),(8,6),(9,7)) S::S [det adv adj n aux neg v det n] -> [det adv adj n v det n neg vpart] (;;alignments: (x1:y1)(x2::y2)(x3::y3)(x4::y4)(x6::y8)(x7::y5)(x7::y9)(x8::y6)(x9::y7)) ;;constraints: ((x1 def) = *+) ((x4 agr) = *3-sing) ((x5 tense) = *past) …. ((y1 def) = *+) ((y3 case) = *nom) ((y4 agr) = *3-sing) …. )

62. October 5, 2004TMI 2004 Panel62 Compositionality - Overview Traverse the c-structure of the English sentence, add compositional structure for translatable chunks Adjust constituent sequences, alignments Remove unnecessary constraints, i.e. those that are contained in the lower-level rule Adjust constraints: use f-structure of correct translation vs. f-structure of incorrect translations to introduce context constraints

63. October 5, 2004TMI 2004 Panel63 Compositionality - Example S::S [det adv adj n aux neg v det n] -> [det adv adj n v det n neg vpart] (;;alignments: (x1:y1)(x2::y2)(x3::y3)(x4::y4)(x6::y8)(x7::y5)(x7::y9)(x8::y6)(x9::y7)) ;;constraints: ((x1 def) = *+) ((x4 agr) = *3-sing) ((x5 tense) = *past) …. ((y1 def) = *+) ((y3 case) = *nom) ((y4 agr) = *3-sing) …. ) S::S [NP aux neg v det n] -> [NP v det n neg vpart] (;;alignments: (x1::y1)(x3::y5)(x4::y2)(x4::y6)(x5::y3)(x6::y4) ;;constraints: ((x2 tense) = *past) …. ((y1 def) = *+) ((y1 case) = *nom) …. ) NP::NP [det AJDP n] -> [det ADJP n] ((x1::y1)… ((y3 agr) = *3-sing) ((x3 agr = *3-sing) ….)

64. October 5, 2004TMI 2004 Panel64 Seeded Version Space Learning: Overview Goal: further generalize the acquired rules Methodology: –Preserve general structural transfer –Consider relaxing specific feature constraints Seed rules are grouped into clusters of similar transfer structure (type, constituent sequences, alignments) Each cluster forms a version space: a partially ordered hypothesis space with a specific and a general boundary The seed rules in a group form the specific boundary of a version space The general boundary is the (implicit) transfer rule with the same type, constituent sequences, and alignments, but no feature constraints

65. October 5, 2004TMI 2004 Panel65 Seeded Version Space Learning NP v det nNP VP … 1.Group seed rules into version spaces as above. 2.Make use of partial order of rules in version space. Partial order is defined via the f-structures satisfying the constraints. 3.Generalize in the space by repeated merging of rules: 1.Deletion of constraint 2.Moving value constraints to agreement constraints, e.g. ((x1 num) = *pl), ((x3 num) = *pl)  ((x1 num) = (x3 num) 4. Check translation power of generalized rules against sentence pairs

66. October 5, 2004TMI 2004 Panel66 Seeded Version Space Learning: Example S::S [NP aux neg v det n] -> [NP v det n neg vpart] (;;alignments: (x1::y1)(x3::y5)(x4::y2)(x4::y6)(x5::y3)(x6::y4) ;;constraints: ((x2 tense) = *past) …. ((y1 def) = *+) ((y1 case) = *nom) ((y1 agr) = *3-sing) … ) ((y3 agr) = *3-sing) ((y4 agr) = *3-sing)… ) S::S [NP aux neg v det n] -> [NP v det n neg vpart] (;;alignments: (x1::y1)(x3::y5)(x4::y2)(x4::y6)(x5::y3)(x6::y4) ;;constraints: ((x2 tense) = *past) … ((y1 def) = *+) ((y1 case) = *nom) ((y1 agr) = *3-plu) … ((y3 agr) = *3-plu) ((y4 agr) = *3-plu)… ) S::S [NP aux neg v det n] -> [NP n det n neg vpart] ( ;;alignments: (x1::y1)(x3::y5) (x4::y2)(x4::y6) (x5::y3)(x6::y4) ;;constraints: ((x2 tense) = *past) … ((y1 def) = *+) ((y1 case) = *nom) ((y4 agr) = (y3 agr)) … )

67. October 5, 2004TMI 2004 Panel67 Preliminary Evaluation English to German Corpus of 141 ADJPs, simple NPs and sentences 10-fold cross-validation experiment Goals: –Do we learn useful transfer rules? –Does Compositionality improve generalization? –Does VS-learning improve generalization?

68. October 5, 2004TMI 2004 Panel68 Summary of Results Average translation accuracy on cross- validation test set was 62% Without VS-learning: 43% Without Compositionality: 57% Average number of VSs: 24 Average number of sents per VS: 3.8 Average number of merges per VS: 1.6 Percent of compositional rules: 34%

69. October 5, 2004TMI 2004 Panel69 Conclusions New paradigm for learning transfer rules from pre-designed elicitation corpus Geared toward languages with very limited resources Preliminary experiments validate approach: compositionality and VS- learning improve generalization

70. October 5, 2004TMI 2004 Panel70 Future Work 1.Larger, more diverse elicitation corpus 2.Additional languages (Mapudungun…) 3.Less information on TL side 4.Reverse translation direction 5.Refine the various algorithms: Operators for VS generalization Generalization VS search Layers for compositionality 6.User interactive verification

71. October 5, 2004TMI 2004 Panel71 Seeded Version Space Learning: Generalization The partial order of the version space: Definition: A transfer rule tr 1 is strictly more general than another transfer rule tr 2 if all f- structures that are satisfied by tr 2 are also satisfied by tr 1. Generalize rules by merging them: –Deletion of constraint –Raising two value constraints to an agreement constraint, e.g. ((x1 num) = *pl), ((x3 num) = *pl)  ((x1 num) = (x3 num))

72. October 5, 2004TMI 2004 Panel72 Seeded Version Space Learning: Merging Two Rules Merging algorithm proceeds in three steps. To merge tr 1 and tr 2 into tr merged : 1.Copy all constraints that are both in tr 1 and tr 2 into tr merged 2.Consider tr 1 and tr 2 separately. For the remaining constraints in tr 1 and tr 2, perform all possible instances of raising value constraints to agreement constraints. 3.Repeat step 1.

73. October 5, 2004TMI 2004 Panel73 Seeded Version Space Learning: The Search The Seeded Version Space algorithm itself is the repeated generalization of rules by merging A merge is successful if the set of sentences that can correctly be translated with the merged rule is a superset of the union of sets that can be translated with the unmerged rules, i.e. check power of rule Merge until no more successful merges