1. Rule Learning - Overview Goal: Syntactic Transfer Rules 1) Flat Seed Generation: produce rules from word- aligned sentence pairs, abstracted only to POS level; no syntactic structure 2) Add compositional structure to Seed Rule by exploiting previously learned rules 3) Seeded Version Space Learning group seed rules by constituent sequences and alignments, seed rules form s-boundary of VS; generalize with validation

2. Flat Seed Generation Create a seed rule that is specific to the sentence pair, but abstracted to the pos level. Use SL information (e.g. parses), and any TL information. E.g.: The highly qualified applicant visited the company. Der äußerst qualifizierte Bewerber besuchte die Firma. ((1,1),(2,2),(3,3),(4,4),(5,5),(6,6)) S::S [det adv adj n v det n]→ [det adv adj n v det n] (;;alignments: (x1::y1) (x2::y2) (x3::y3) (x4::y4) (x5::y5) (x6::y6) (x7::y7) ;;constraints: ((x1 def) = *+) ((x4 agr) = *3-sing) ((x5 tense) = *past) …. ((y1 def) = *+) ((y3 case) = *nom) ((y4 agr) = *3sg) … )

3. Compositionality If there is a previously learned rule that can account for part of the sentence, adjust seed rule to reflect this compositional element. Adjust constituent sequences, alignments, and constraints: add context constraints (from possible translations), remove unnecessary ones S::S [det adv adj n v det n]→ [det adv adj n v det n] (;;alignments: (x1::y1) (x2::y2) (x3::y3) (x4::y4) (x5::y5) (x6::y6) (x7::y7) ;;constraints: ((x1 def) = *+) ((x4 agr) = *3-sing) ((x5 tense) = *past) …. ((y1 def) = *+) ((y4 agr) = *3sg) … ) S::S [NP v det n]→ [NP v det n] (;;alignments: (x1::y1) (x2::y2) (x3::y3) (x4::y4) (x5::y5) (x6::y6) (x7::y7) ;;constraints: ((x5 tense) = *past) …. ((y1 def) = *+) ((y1 case) = *nom) ((y1 agr) = *3sg) … ) NP::NP [det adv adj n] [det adv adj n] ((x1::y1)… ((y4 agr) = (x4 agr) ….)

4. 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

5. Future Work Baseline evaluation Adjust generalization step size Revisit generalization operators Introduce specialization operators to retract from overgeneralizations (including seed rules) Learn from an unstructured bilingual corpus Evaluate merges to pick the optimal one at any step: based on cross-validation, number of sentences it can translate