October 10, 2003BLTS Kickoff Meeting1 Transfer with Strong Decoding Learning Module Transfer Rules {PP,4894} ;;Score: 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

October 10, 2003BLTS Kickoff Meeting2 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)) )

October 10, 2003BLTS Kickoff Meeting3 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

October 10, 2003BLTS Kickoff Meeting4 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))

October 10, 2003BLTS Kickoff Meeting5 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))

October 10, 2003BLTS Kickoff Meeting6 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))

October 10, 2003BLTS Kickoff Meeting7 Examples of Learned Rules {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) )

October 10, 2003BLTS Kickoff Meeting8 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) )

October 10, 2003BLTS Kickoff Meeting9 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

October 10, 2003BLTS Kickoff Meeting10 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

October 10, 2003BLTS Kickoff Meeting11 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!!

October 10, 2003BLTS Kickoff Meeting12 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)

October 10, 2003BLTS Kickoff Meeting13 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

October 10, 2003BLTS Kickoff Meeting14 Effect of Reordering in the Decoder

October 10, 2003BLTS Kickoff Meeting15 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

October 10, 2003BLTS Kickoff Meeting16 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”

October 10, 2003BLTS Kickoff Meeting17 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

October 10, 2003BLTS Kickoff Meeting18 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”