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Enriched translation model using morphology in MT

Enriched translation model using morphology in MT. Luong Minh Than g WING group meeting – 07 July, 2009. Overview. Brief recap on SMT & morphological analysis Motivation Enriched translation model Twin phrase-table construction Merging phrase tables Experiments Conclusion.

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Enriched translation model using morphology in MT

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  1. Enriched translation model using morphology in MT Luong Minh Thang WING group meeting – 07 July, 2009

  2. Overview • Brief recap on SMT & morphological analysis • Motivation • Enriched translation model • Twin phrase-table construction • Merging phrase tables • Experiments • Conclusion

  3. SMT overview – alignment • Parallel data Ensinnäkin kohtaamiemme taloudellisten ja sosiaalisten vaikeuksien vuoksi on havaittavissa huolestumista , vaikka kasvu on kestävällä pohjalla ja tulosta vuosien ponnisteluista , kaikkien kansalaistemme taholta . These are , first and foremost , messages of concern at the economic and social problems that we are experiencing , in spite of a period of sustained growth stemming from years of efforts by all our fellow citizens . • Alignment: one-to-many (1-M) Source Target

  4. SMT overview – translation model • Intersect alignment 1-M + M-1  M – M • Extracting phrases from M-M alignment  translation model (phrase table). problems ||| ongelmat ||| 0.372611 0.597858 0.114146 0.13882 2.718 problems ||| ongelmasta ||| 0.352941 0.423077 0.000836237 0.0012435 2.718 … problems ||| vaikeuksista ||| 0.0696946 0.105991 0.0124042 0.0130002 2.718 problems ||| vaikeuksien ||| 0.0410959 0.062069 0.000836237 0.0010174 2.718 Translation probabilities Lexical probabilities Phrase penalty Foreign f English e

  5. Recap - Morphological analysis • Morpheme: minimal meaning-bearing unit English: machine + s, present + ed, etc. Finnish: oppositio + kansa + n + edusta + ja = opposition of parliament member • Morfessor (Creutz & Lagus, 2007): segment words, unsupervised manner un/PRE + fortunate/STM + ly/SUF

  6. Motivation • Problem: • Multiple word forms in morphology-complex language, e.g. ongelmat, ongelmasta, etc. • Rare words often occur and are hard to align  incorrect entries in normal (word-align) phrase table. • Solution: • Construct morpheme-align phrase table (PT) to aggregate better statistics for rare words. • Combine word- and morpheme-align PTs to produce even better translation model in a proper way.

  7. Overview • Brief recap on SMT & morphological analysis • Motivation • Enriched translation model • Twin phrase-table construction • Merging phrase tables • Experiments • Conclusion

  8. Twin phrase-table (PT) construction Word Morpheme GIZA++ GIZA++ Word alignment Morpheme alignment Phrase Extraction Phrase Extraction problem/STM+ s/SUF ||| ongelma/STM+ t/SUF problems ||| vaikeuksista PTm PTw Morphological segmentation PTwm problem/STM+ s/SUF ||| vaikeu/STM+ ksi/SUF+ sta/SUF PT merging Decoding

  9. Existing PT-merging methods • Add-feature - (Nakov, 2008; Chen et. al. 2009): F1 = F2 = F3 =  heuristic-driven • Interpolation - (Wu & Wang, 2007) : • tran(f|e) = α * tran1(f|e) + (1- α) * tran2(f|e) • lex(f|e) = β * lex1(f|e) + (1- β) * lex2(f|e)  not consider score “meaning” 1 if from 1st PT 1 if from 2nd PT 1 if from both PTs 0.5 otherwise 0.5 otherwise 0.5 otherwise

  10. Our merging method – normalizing translation probabilities problem + s ||| ongelma + t problem + s ||| ongelma + t problem + s ||| ongelma + t problem + s ||| vaikeu + ksi + sta problem + s ||| vaikeu + ksi + sta problem + s ||| ongelma + sta PTwm PTm MLE tran1(e|f) =count1(e, f) / ∑e count1(e, f) tran2(e|f) =count2(e, f) / ∑e count2(e, f)

  11. Our merging method – normalizing translation probabilities problem + s ||| ongelma + t problem + s ||| ongelma + t problem + s ||| ongelma + t problem + s ||| vaikeu + ksi + sta problem + s ||| vaikeu + ksi + sta problem + s ||| ongelma + sta PTwm PTm MLE tran(vaikeuksista | problems) =1/2=0.5 tran(ongelmasta | problems) =1/2=0.5 tran(ongelmat | problems) = 3/4 = 0.75 tran(vaikeuksista | problems) = 1/4 = 0.25 Interpolation (ratio = 0.5) tran(vaikeuksista | problems) = (0.5 + 0.25)/2 = 0.375 tran(ongelmat | problems) = (0 + 0.75)/2 = 0.375 tran(ongelmasta | problems) = (0.5 + 0)/2 = 0.25 Undesired translation!

  12. Our merging method – normalizing translation probabilities problem + s ||| ongelma + t problem + s ||| ongelma + t problem + s ||| ongelma + t problem + s ||| vaikeu + ksi + sta problem + s ||| vaikeu + ksi + sta problem + s ||| ongelma + sta PTwm PTm MLE tran1(e|f) =count1(e, f) / ∑e count1(e, f) tran2(e|f) =count2(e, f) / ∑e count2(e, f) Normalization tran(e|f) =[ count1(e, f) + count2(e, f)] / [ ∑e count1(e, f) + ∑e count2(e, f) ]

  13. Our merging method – normalizing translation probabilities problem + s ||| ongelma + t problem + s ||| ongelma + t problem + s ||| ongelma + t problem + s ||| vaikeu + ksi + sta problem + s ||| vaikeu + ksi + sta problem + s ||| ongelma + sta PTwm PTm MLE tran(vaikeuksista | problems) =1/2=0.5 tran(ongelmasta | problems) =1/2=0.5 tran(ongelmat | problems) = 3/4 = 0.75 tran(vaikeuksista | problems) = 1/4 = 0.25 Normalization tran(vaikeuksista | problems) = (1 + 1)/(2+4) = 0.33 tran(ongelmat | problems) = (0 + 3)/(2 + 4) = 0.5 tran(ongelmasta | problems) = (1 + 0)/(2 + 4) = 0.17 Desired translation!

  14. Our merging method – full lexical probability interpolation lex(vaikeuksista | problems) = w1 lex(ongelmasta | problems) = w2 lex(vaikeu + ksi + sta | problem + s) = m1 lex(ongelma + t | problem + s) = m3 PTw lexical model PTm lexical model P(vaikeuksista|problems) P(ongelmasta|problems) P(vaikeu|problem), P(ongelma|problem), P(t|s), P(ksi|s),P(sta|s) Normal Interpolation (ratio = 0.5) Missing interpolated probabilities ! lex(vaikeuksista | problems) = (w1 + m1)/2 lex(ongelmat | problems) = (w2 + 0)/2 lex(ongelmasta | problems) = (0 + m3) /2 • Estimate lex(ongelma + sta | problem + s) using PTm lexical model  m2 • Estimate lex(ongelmat | problems) using PTw lexical model  w3 Full Interpolation

  15. Overview • Brief recap on SMT & morphological analysis • Motivation • Enriched translation model • Twin phrase-table construction • Merging phrase tables • Experiments • Conclusion

  16. Experiments – dataset • 2005 ACL shared task (Koehn & Monz, 2005)

  17. Experiments – baselines • w-system: uses PTw translate at word-level • m-system: uses PTm translate at morpheme-level • m-BLEU: BLEU where each token unit is a morpheme

  18. Experiments – our system • Improvements over m-system and w-system are statistically significant using sign test by (Collins et al. 2005)

  19. Conclusion Our contributions: • Enrich the translation model without using additional data. • Propose a principal way to merge phrase tables generated at different granularities.

  20. Q & A • Thank you !!!

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