The AVENUE Project: Automatic Rule Learning for Resource-Limited Machine Translation Faculty: Alon Lavie, Jaime Carbonell, Lori Levin, Ralf Brown Students: Katharina Probst, Erik Peterson, Christian Monson, Ariadna Font-Llitjos, Rachel Reynolds

Sept 11, 2003LTI 2003 IC2 Why Machine Translation for Minority and Indigenous Languages? Commercial MT economically feasible for only a handful of major languages with large resources (corpora, human developers) Is there hope for MT for languages with limited resources? Benefits include: –Better government access to indigenous communities (Epidemics, crop failures, etc.) –Better indigenous communities participation in information-rich activities (health care, education, government) without giving up their languages. –Language preservation –Civilian and military applications (disaster relief)

Sept 11, 2003LTI 2003 IC3 MT for Minority and Indigenous Languages: Challenges Minimal amount of parallel text Possibly competing standards for orthography/spelling Often relatively few trained linguists Access to native informants possible Need to minimize development time and cost

Sept 11, 2003LTI 2003 IC4 AVENUE Partners LanguageCountryInstitutions Mapudungun (in place) Chile Universidad de la Frontera, Institute for Indigenous Studies, Ministry of Education Quechua (discussion) Peru Ministry of Education Iñupiaq (discussion) US (Alaska) Ilisagvik College, Barrow school district, Alaska Rural Systemic Initiative, Trans-Arctic and Antarctic Institute, Alaska Native Language Center Siona (discussion) Colombia OAS-CICAD, Plante, Department of the Interior

Sept 11, 2003LTI 2003 IC5 AVENUE: Two Technical Approaches Generalized EBMT Parallel text 50K- 2MB (uncontrolled corpus) Rapid implementation Proven for major L’s with reduced data Transfer-rule learning Elicitation (controlled) corpus to extract grammatical properties Seeded version- space learning

Sept 11, 2003LTI 2003 IC6 AVENUE Architecture User Learning Module Elicitation Process SVS Learning Process Transfer Rules Run-Time Module SL Input SL Parser Transfer Engine TL Generator EBMT Engine Multi-Engine Decoder TL Output

Sept 11, 2003LTI 2003 IC7 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

Sept 11, 2003LTI 2003 IC8 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

Sept 11, 2003LTI 2003 IC9 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”

Sept 11, 2003LTI 2003 IC10 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)) )

Sept 11, 2003LTI 2003 IC11 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)) )

Sept 11, 2003LTI 2003 IC12 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)

Sept 11, 2003LTI 2003 IC13 Examples of Learned Rules (I) {NP,14244} ;;Score: NP::NP [N] -> [DET N] ( (X1::Y2) ) {NP,14434} ;;Score: NP::NP [ADJ CONJ ADJ N] -> [ADJ CONJ ADJ N] ( (X1::Y1) (X2::Y2) (X3::Y3) (X4::Y4) ) {PP,4894} ;;Score: PP::PP [NP POSTP] -> [PREP NP] ( (X2::Y1) (X1::Y2) )

Sept 11, 2003LTI 2003 IC14 A Limited Data Scenario for Hindi-to-English Put together a scenario with “miserly” data resources: –Elicited Data corpus: 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!!

Sept 11, 2003LTI 2003 IC15 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)

Sept 11, 2003LTI 2003 IC16 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) )

Sept 11, 2003LTI 2003 IC17 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

Sept 11, 2003LTI 2003 IC18 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!

Sept 11, 2003LTI 2003 IC19 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)

Sept 11, 2003LTI 2003 IC20 Results on JHU Test Set (very miserly training data) SystemBLEUM-BLEUNIST EBMT SMT XFER (naïve) man grammar XFER (strong) no grammar XFER (strong) learned grammar XFER (strong) man grammar XFER+SMT

Sept 11, 2003LTI 2003 IC21 Effect of Reordering in the Decoder

Sept 11, 2003LTI 2003 IC22 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: Token coverage with morphology: 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

Sept 11, 2003LTI 2003 IC23 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”

Sept 11, 2003LTI 2003 IC24 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

Sept 11, 2003LTI 2003 IC25 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

Sept 11, 2003LTI 2003 IC26 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

Sept 11, 2003LTI 2003 IC27 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)

Sept 11, 2003LTI 2003 IC28 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) …. )

Sept 11, 2003LTI 2003 IC29 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

Sept 11, 2003LTI 2003 IC30 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) ….)

Sept 11, 2003LTI 2003 IC31 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

Sept 11, 2003LTI 2003 IC32 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

Sept 11, 2003LTI 2003 IC33 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)) … )

Sept 11, 2003LTI 2003 IC34 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?

Sept 11, 2003LTI 2003 IC35 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%

Sept 11, 2003LTI 2003 IC36 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

Sept 11, 2003LTI 2003 IC37 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

Sept 11, 2003LTI 2003 IC38 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))

Sept 11, 2003LTI 2003 IC39 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.

Sept 11, 2003LTI 2003 IC40 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