Download presentation

Presentation is loading. Please wait.

Published byAgustin Lenington Modified over 3 years ago

1
Rule Learning – Overview Goal: learn transfer rules for a language pair where one language is resource-rich, the other is resource-poor Learning proceeds in three steps: 1.Flat Seed Generation: informed guessing of transfer rules 2.Compositionality: adding structure to rules, using previously learned rules 3.Seeded Version Space Learning: generalizing rules to make them scale to more unseen examples

2
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) …. ) 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)) Flat Seed Generation - Example

3
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) ….) Compositionality - Example

4
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)) … ) Seeded Version Space Learning - Example

5
Remaining Research Issues Improvement of existing algorithms Reversal of translation direction Learning with less information on the resource-poor language Learning from an unstructured corpus

Similar presentations

OK

CS 478 – Tools for Machine Learning and Data Mining Clustering: Distance-based Approaches.

CS 478 – Tools for Machine Learning and Data Mining Clustering: Distance-based Approaches.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Carta post ppt online Ppt on biomass energy india Ppt on the road not taken frost Ppt on icici bank history Ppt on wireless sensor networks applications Maths ppt on circles for class 10 download Ppt on applied operational research consultants Ppt on pricing policy for new products Ppt on mammals and egg laying animals Ppt on forward rate agreement quote