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Discriminative Modeling extraction Sets for Machine Translation

Discriminative Modeling extraction Sets for Machine Translation. Author John DeNero and Dan Klein UC Berkeley Presenter Justin Chiu. Contribution. Extraction set Nested collections of all the overlapping phrase pairs consistent with an underlying word-alignment

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Discriminative Modeling extraction Sets for Machine Translation

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  1. Discriminative Modeling extraction Sets for Machine Translation Author John DeNero and Dan Klein UC Berkeley Presenter Justin Chiu

  2. Contribution • Extraction set • Nested collections of all the overlapping phrase pairs consistent with an underlying word-alignment • Advantages over word-factored alignment model • Can incorporate features on phrase pairs, more than word link • Optimize a extraction-based loss function really direct to generating translation • Perform better than both supervised and unsupervised baseline

  3. Progress of Statistical MT • Generate translated sentences word by word • Using while fragments of training example, building translation rules • Aligned at the word level • Extract fragment-level rules from word aligned sentence pair • Tree to string translation • Extraction Set Models • Set of all overlapping phrasal translation rule + alignment

  4. Outline • Extraction Set Models • Model Estimation • Model Inference • Experiments

  5. Extraction set models

  6. Extraction Set Models • Input • Unaligned sentence • Output • Extraction set of phrasal translation rules • Word alignment

  7. Extraction Sets from Word Alignments

  8. Extraction Sets from Word Alignments

  9. Extraction Sets from Word Alignments

  10. Possible and Null Alignment Links • Possible links has two types • Function words that is unique in its language • Short phrase that has no lexical equivalent • Null alignment • Express content that isabsent in its translation

  11. Interpreting Possible and Null Alignment Links

  12. Interpreting Possible and Null Alignment Links

  13. Linear Model for Extraction Set

  14. Scoring Extraction Sets

  15. Model Estimation

  16. MIRA(Margin-infused Relaxed Algorithm)

  17. Extraction Set Loss Function

  18. Model Inference

  19. Possible Decompositions

  20. DP for Extraction Sets

  21. DP for Extraction Sets

  22. Finding Pseudo-Gold ITG Alignment

  23. Experiments

  24. Five systems for comparison • Unsupervised baseline • Giza++ • Joint HMM • Supervised baseline • Block ITG • Extraction Set Coarse Pass • Does not score bispans that corss bracketing of ITG derivations • Full Extraction Set Model

  25. Data • Discriminative training and alignment evaluation • Trained baseline HMM on 11.3 million words of FBIS newswire data • Hand-aligned portion of the NIST MT02 test set • 150 training and 191 test sentences • End-to-end translation experiments • Trained on 22.1 million word prarllel corpus consisting of sentence up to 40 of newswire data from GALE program • NIST MT04/MT05 test sets

  26. Results

  27. Discussion • Syntax labels v.s words • Word align to rule  Rule to word align • Information from two directions • 65% of type 1 error

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