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Mixed syntax translation

Hieu Hoang MT Marathon 2011 Trento. Mixed syntax translation. Contents. What is a syntactic model? What’s wrong with Syntax? Which syntax model to use? Why use syntactic models? Mixed-Syntax Model Extraction Decoding Results Future Work. What is a syntactic model?.

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Mixed syntax translation

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  1. Hieu Hoang MT Marathon 2011 Trento Mixed syntax translation

  2. Contents • What is a syntactic model? • What’s wrong with Syntax? • Which syntax model to use? • Why use syntactic models? • Mixed-Syntax Model • Extraction • Decoding • Results • Future Work

  3. What is a syntactic model? • Hierarchical Phrase-Based Model • String-to-string • Non-terminals are unlabelled • Tree-to-string Model • Source non-terminals are labelled • match input parse tree X habe X1gegessen # have eaten X1 ShabeNP1gegessen # have eaten NP1

  4. What is a syntactic model? • Hierarchical Phrase-Based Model • String-to-string • Non-terminals are unlabelled • Tree-to-string Model • Source non-terminals are labelled • match input parse tree X habe X1gegessen# have eaten X1 ShabeNP1gegessen # have eaten NP1

  5. What is a syntactic model? • Hierarchical Phrase-Based Model • String-to-string • Non-terminals are unlabelled • Tree-to-string Model • Source non-terminals are labelled • match input parse tree X habe X1gegessen # have eaten X1 ShabeNP1gegessen # have eaten NP1

  6. What is a syntactic model? • Hierarchical Phrase-Based Model • String-to-string • Non-terminals are unlabelled • Tree-to-string Model • Source non-terminals are labelled • match input parse tree XhabeX1gegessen # have eaten X1 ShabeNP1gegessen # have eaten NP1

  7. What is a syntactic model? • Hierarchical Phrase-Based Model • String-to-string • Non-terminals are unlabelled • Tree-to-string Model • Source non-terminals are labelled • match input parse tree X habe X1gegessen # have eaten X1 ShabeNP1gegessen # have eaten NP1

  8. Contents • What is a syntactic model? • What’s Wrong with Syntax? • Which syntax model to use? • Why use syntactic models? • Mixed-Syntax Model • Extraction • Decoding • Results • Future Work

  9. What’s Wrong with Syntax? Evaluation of French-English MT System (Ambati and Lavie, 2009)

  10. Hierarchical Model according to JánosVeres , this would be in the first quarter of 2008 possible .

  11. Hierarchical Model according to JánosVeres , this would be in the first quarter of 2008 possible .

  12. Hierarchical Model according to JánosVeres , this would be in the first quarter of 2008 possible .

  13. Tree-to-String Model NE NE VAFIN PDS APPRART ADJA NN CARD ADJA APPR PUNC PN PP PP S TOP

  14. Tree-to-String Model János Veres would be this in the first quarter of 2008 possible according to . NE NE VAFIN PDS APPRART ADJA NN CARD ADJA APPR PUNC PN PP PP S TOP

  15. Tree-to-String Model János Veres would be this in the first quarter of 2008 possible according to . NE NE VAFIN PDS APPRART ADJA NN CARD ADJA APPR PUNC PN PP PP [X,1] [X,2] [X,1] [X,2] [X,1] [X,2] [X,1] [X,2] [X,1] [X,2] [X,1] [X,2] [X,1] [X,2] [X,1] [X,2] [X,1] [X,2] S TOP

  16. Contents • What’s Wrong with Syntax? • Which syntax model to use? • Why use syntactic models? • Mixed-Syntax Model • Extraction • Decoding • Results • Future Work

  17. Other Syntactic Models • Syntax-Augmented MT (SAMT) • Not constrained only to parse tree • (Zollmann and Venugopal, 2006) • Binarization • Restructure and relabel parse parse tree • (Wang et al, 2010) • Forest-based translation • Recover from parse errors • (Mi et al, 2008) • Soft constraint • Reward/Penalize derivations which follows parse structure • (Chiang 2010)

  18. Other Syntactic Models • Syntax-Augmented MT (SAMT) • Not constrained only to parse tree • (Zollmann and Venugopal, 2006) • Binarization • Restructure and relabel parse parse tree • (Wang et al, 2010) • Forest-based translation • Recover from parse errors • (Mi et al, 2008) • Soft constraint • Reward/Penalize derivations which follows parse structure • (Chiang 2010)

  19. Other Syntactic Models • Syntax-Augmented MT (SAMT) • Not constrained only to parse tree • (Zollmann and Venugopal, 2006) • Binarization • Restructure and relabel parse parse tree • (Wang et al, 2010) • Forest-based translation • Recover from parse errors • (Mi et al, 2008) • Soft constraint • Reward/Penalize derivations which follows parse structure • (Chiang 2010)

  20. Other Syntactic Models • Syntax-Augmented MT (SAMT) • Not constrained only to parse tree • (Zollmann and Venugopal, 2006) • Binarization • Restructure and relabel parse parse tree • (Wang et al, 2010) • Forest-based translation • Recover from parse errors • (Mi et al, 2008) • Soft constraint • Reward/Penalize derivations which follows parse structure • (Chiang 2010)

  21. Other Syntactic Models • Syntax-Augmented MT (SAMT) • Not constrained only to parse tree • (Zollmann and Venugopal, 2006) • Binarization • Restructure and relabel parse parse tree • (Wang et al, 2010) • Forest-based translation • Recover from parse errors • (Mi et al, 2008) • Soft constraint • Reward/Penalize derivations which follows parse structure • (Chiang 2010)

  22. Contents • What’s Wrong with Syntax? • Which syntax model to use? • Why use syntactic models? • Mixed-Syntax Model • Extraction • Decoding • Results • Future Work

  23. Why Use Syntactic Models? • Decrease decoding time • Derivation constrained by source parse tree

  24. Why Use Syntactic Models? • Decrease decoding time • Derivation constrained by source parse tree • Long-range reordering during decoding • rules covering more words than max-span limit

  25. Why Use Syntactic Models? • Decrease decoding time • Derivation constrained by source parse tree • Long-range reordering during decoding • rules covering more words than max-span limit • Other rule-forms • 3+ non-terminals • consecutive non-terminals • non-lexicalized rules

  26. Why Use Syntactic Models? • Decrease decoding time • Derivation constrained by source parse tree • Long-range reordering during decoding • rules covering more words than max-span limit • Other rule-forms • 3+ non-terminals • consecutive non-terminals • non-lexicalized rules X  S1O2V3# S1V3O2 X  PRO1 PRO2 aime bien # PRO1 like PRO2

  27. Contents • What’s Wrong with Syntax? • Which syntax model to use? • Why use syntactic models? • Mixed-Syntax Model • Extraction • Decoding • Results • Future Work

  28. Other Syntactic Models • Syntax-Augmented MT (SAMT) • Not constrained only to parse tree • (Zollmann and Venugopal, 2006) • Binarization • Restructure and relabel parse parse tree • (Wang et al, 2010) • Forest-based translation • Recover from parse errors • (Mi et al, 2008) • Soft constraint • Reward/Penalize derivations which follows parse structure • (Chiang 2010)

  29. Other Syntactic Models • Syntax-Augmented MT (SAMT) • Not constrained only to parse tree • (Zollmann and Venugopal, 2006) • Binarization • Restructure and relabel parse parse tree • (Wang et al, 2010) • Forest-based translation • Recover from parse errors • (Mi et al, 2008) • Soft constraint • Reward/Penalize derivations which follows parse structure • (Chiang 2010) • Ignore Syntax (occasionally)

  30. Mixed-Syntax Model • Tree-to-string model • input is a parse tree • Roles of non-terminals • Constrain derivation to parse constituents • State information • Consistent node label on target derivation • hypotheses with different head NT cannot be recombined

  31. Mixed-Syntax Model • Tree-to-string model • input is a parse tree • Roles of non-terminals • Constrain derivation to parse constituents • Can sometime have no constraints • State information • Consistent node label on target derivation • hypotheses with different head NT cannot be recombined • always X

  32. Mixed-Syntax Model Example Translation Rules • Naïve syntax model • Mixed-Syntax Model VP  VVFIN1zu VVINF2 # to VVFIN2 VVINF1 VP X1zu VVINF2 # X to X2 X1

  33. Mixed-Syntax Model Example Translation Rules • Naïve syntax model • Mixed-Syntax Model VP  VVFIN1zu VVINF2 # to VVFIN2 VVINF1 VP X1zu VVINF2 # X to X2X1

  34. Mixed-Syntax Model Example Translation Rules • Naïve syntax model • Mixed-Syntax Model VP  VVFIN1zu VVINF2 # to VVFIN2 VVINF1 VP X1zu VVINF2 # X to X2 X1

  35. Contents • What’s Wrong with Syntax? • Which syntax model to use? • Why use syntactic models? • Mixed-Syntax Model • Extraction • Decoding • Results • Future Work

  36. Extraction • Allow rules • Max 3 non-terminals • Adjacent non-terminals • At least 1 NT must be syntactic • Non-lexicalized rules

  37. Example Rules Extracted

  38. Contents • What’s Wrong with Syntax? • Which syntax model to use? • Why use syntactic models? • Mixed-Syntax Model • Extraction • Decoding • Results • Future Work

  39. Synchronous CFG Input:

  40. Synchronous CFG Input: Rules: S NP1 VP2 # NP1 VP2 NP je # I PRO lui # him VB vu # see VP ne PRO1 VB2 pas # did not VB2 PRO1

  41. Synchronous CFG Input: Derivation: Rules: S NP1 VP2 # NP1 VP2 NP je # I PRO lui # him VB vu # see VP ne PRO1 VB2 pas # did not VB2 PRO1

  42. Synchronous CFG Input: Derivation: Rules: S NP1 VP2 # NP1 VP2 NP je # I PRO lui # him VB vu # see VP ne PRO1 VB2 pas # did not VB2 PRO1

  43. Synchronous CFG Input: Derivation: Rules: S NP1 VP2 # NP1 VP2 NP je # I PRO lui # him VB vu # see VP ne PRO1 VB2 pas # did not VB2 PRO1

  44. Synchronous CFG Input: Derivation: Rules: S NP1 VP2 # NP1 VP2 NP je # I PRO lui # him VB vu # see VP ne PRO1 VB2 pas # did not VB2 PRO1

  45. Synchronous CFG Input: Derivation: Rules: S NP1 VP2 # NP1 VP2 NP je # I PRO lui # him VB vu # see VP ne PRO1 VB2 pas # did not VB2 PRO1

  46. Synchronous CFG Input: Derivation: Rules: S NP1 VP2 # NP1 VP2 NP je # I PRO lui # him VB vu # see VP ne PRO1 VB2 pas # did not VB2 PRO1

  47. Mixed-Syntax Model Input:

  48. Mixed-Syntax Model Input: Rules: S NP1 VP2 # X  NP1 VP2 PRO je # X  I PRO lui # X him VB vu # X  see VP ne X1 pas # did not X1 X  PRO1 VB2 # X  X2 X1

  49. Mixed-Syntax Model Derivation: Input: Rules: S NP1 VP2 # X  NP1 VP2 PRO je # X  I PRO lui # X him VB vu # X  see VP ne X1 pas # did not X1 X  PRO1 VB2 # X  X2 X1

  50. Mixed-Syntax Model Derivation: Input: Rules: S NP1 VP2 # X  NP1 VP2 PRO je # X  I PRO lui # X him VB vu # X  see VP ne X1 pas # did not X1 X  PRO1 VB2 # X  X2 X1

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