Structured ramp loss minimization for machine translation

NAACL HLT 2012 - 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Volumn , Issue , 2012, Pages 221-231

  Gimpel, Kevin ^a   Smith, Noah A ^a  

^a Carnegie Mellon University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; COMPUTER AIDED LANGUAGE TRANSLATION; LEARNING ALGORITHMS; MACHINE LEARNING;

EMPIRICAL RISK MINIMIZATION; LOSS FUNCTIONS; LOSS MINIMIZATION ALGORITHMS; LOSSES MINIMIZATIONS; MACHINE TRANSLATIONS; MACHINE-LEARNING; RANDOM SEEDS; STATISTICAL MACHINE TRANSLATION; TRAINING ALGORITHMS;

MACHINE TRANSLATION;

EID: 84903444552     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (51)

1. A. Arun and P. Koehn. 2007. Online learning methods for discriminative training of phrase based statistical machine translation. In Proc. of MT Summit XI.
2. P. Blunsom and M. Osborne. 2008. Probabilistic inference for machine translation. In Proc. of EMNLP.
3. P. Blunsom, T. Cohn, and M. Osborne. 2008. A discriminative latent variable model for statistical machine translation. In Proc. of ACL.
4. D. Cer, D. Jurafsky, and C. Manning. 2008. Regularization and search for minimum error rate training. In Proc. of ACL-2008 Workshop on Statistical Machine Translation.
5. D. Cer. 2011. Parameterizing Phrase Based Statistical Machine Translation Models: An Analytic Study. Ph.D. thesis, Stanford University.
6. P. Chang, M. Galley, and C. Manning. 2008. Optimizing Chinese word segmentation for machine translation performance. In Proc. of ACL-2008 Workshop on Statistical Machine Translation.
7. S. Chen and J. Goodman. 1998. An empirical study of smoothing techniques for language modeling. Technical report 10-98, Harvard University.
8. C. Cherry and G. Foster. 2012. Batch tuning strategies for statistical machine translation. In Proc. of NAACL.
9. D. Chiang, Y. Marton, and P. Resnik. 2008. Online largemargin training of syntactic and structural translation features. In Proc. of EMNLP.
10. D. Chiang, W. Wang, and K. Knight. 2009. 11,001 new features for statistical machine translation. In Proc. of NAACL-HLT.
11. D. Chiang. 2005. A hierarchical phrase-based model for statistical machine translation. In Proc. of ACL.
12. D. Chiang. 2007. Hierarchical phrase-based translation. Computational Linguistics, 33(2):201-228.
13. J. H. Clark, C. Dyer, A. Lavie, and N. A. Smith. 2011. Better hypothesis testing for statistical machine translation: Controlling for optimizer instability. In Proc. of ACL.
14. M. Collins. 2002. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proc. of EMNLP.
15. R. Collobert, F. Sinz, J. Weston, and L. Bottou. 2006. Trading convexity for scalability. In ICML.
16. K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. 2006. Online passive-aggressive algorithms. Journal of Machine Learning Research, 7:551-585.
17. C. B. Do, Q. Le, C. H. Teo, O. Chapelle, and A. Smola. 2008. Tighter bounds for structured estimation. In Proc. of NIPS.
18. R. O. Duda and P. E. Hart. 1973. Pattern classification and scene analysis. John Wiley, New York.
19. G. Foster and R. Kuhn. 2009. Stabilizing minimum error rate training. In Proc. of Fourth Workshop on Statistical Machine Translation.
20. K. Gimpel and N. A. Smith. 2010. Softmax-margin CRFs: Training log-linear models with cost functions. In Proc. of NAACL.
21. M. Hopkins and J. May. 2011. Tuning as ranking. In Proc. of EMNLP.
22. B. H. Juang, W. Chou, and C. H. Lee. 1997. Minimum classification error rate methods for speech recognition. Speech and Audio Processing, IEEE Transactions on, 5(3):257-265, may.
23. J. Kaiser, B. Horvat, and Z. Kacic. 2000. A novel loss function for the overall risk criterion based discriminative training of hmm models. In Proc. of ICSLP.
24. P. Koehn, F. J. Och, and D. Marcu. 2003. Statistical phrase-based translation. In Proc. of HLT-NAACL.
25. P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, and E. Herbst. 2007. Moses: Open source toolkit for statistical machine translation. In Proc. of ACL (demo session).
26. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. of ICML.
27. Y. Lee, K. Papineni, S. Roukos, O. Emam, and H. Hassan. 2003. Language model based Arabic word segmentation. In Proc. of ACL.
28. Z. Li and J. Eisner. 2009. First- and second-order expectation semirings with applications to minimum-risk training on translation forests. In Proc. of EMNLP.
29. P. Liang, A. Bouchard-Côté, D. Klein, and B. Taskar. 2006. An end-to-end discriminative approach to machine translation. In Proc. of COLING-ACL.
30. Chin-Yew Lin and Franz Josef Och. 2004. Orange: a method for evaluating automatic evaluation metrics for machine translation. In Proc. of Coling.
31. W. Macherey, F. Och, I. Thayer, and J. Uszkoreit. 2008. Lattice-based minimum error rate training for statistical machine translation. In EMNLP.
32. A. F. T. Martins, K. Gimpel, N. A. Smith, E. P. Xing, P. M. Q. Aguiar, andMA. T. Figueiredo. 2010. Learning structured classifiers with dual coordinate descent. Technical report, Carnegie Mellon University.
33. D. McAllester and J. Keshet. 2011. Generalization bounds and consistency for latent structural probit and ramp loss. In Proc. of NIPS.
34. D. McAllester, T. Hazan, and J. Keshet. 2010. Direct loss minimization for structured prediction. In Proc. of NIPS.
35. R. C. Moore and C. Quirk. 2008. Random restarts in minimum error rate training for statistical machine translation. In Proc. of Coling.
36. F. J. Och and H. Ney. 2002. Discriminative training and maximum entropy models for statistical machine translation. In Proc. of ACL.
37. F. J. Och and H. Ney. 2003. A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1).
38. F. J. Och. 2003. Minimum error rate training for statistical machine translation. In Proc. of ACL.
39. K. Papineni, S. Roukos, T. Ward, and W.J. Zhu. 2001. BLEU: a method for automatic evaluation of machine translation. In Proc. of ACL.
40. D. Povey and P. C. Woodland. 2002. Minimum phone error and I-smoothing for improved discrimative training. In Proc. of ICASSP.
41. A. Quattoni, M. Collins, and T. Darrell. 2004. Conditional random fields for object recognition. In NIPS 17.
42. N. Ratliff, J. A. Bagnell, and M. Zinkevich. 2006. Subgradient methods for maximum margin structured learning. In ICML Workshop on Learning in Structured Output Spaces.
43. R. Rosenfeld. 1996. A maximum entropy approach to adaptive statistical language modeling. Computer, Speech and Language, 10(3).
44. D. A. Smith and J. Eisner. 2006. Minimum risk annealing for training log-linear models. In Proc. of COLING-ACL.
45. A. Stolcke. 2002. SRILM-an extensible language modeling toolkit. In Proc. of ICSLP.
46. B. Taskar, C. Guestrin, and D. Koller. 2003. Max-margin Markov networks. In Advances in NIPS 16.
47. V. Vapnik. 1998. Statistical learning theory. Wiley.
48. T. Watanabe, J. Suzuki, H. Tsukada, and H. Isozaki. 2007. Online large-margin training for statistical machine translation. In Proc. of EMNLP-CoNLL.
49. C. J. Yu and T. Joachims. 2009. Learning structural SVMs with latent variables. In Proc. of ICML.
50. A. L. Yuille and Anand Rangarajan. 2002. The concaveconvex procedure (CCCP). In Proc. of NIPS. MIT Press.
51. R. Zens, S. Hasan, and H. Ney. 2007. A systematic comparison of training criteria for statistical machine translation. In Proc. of EMNLP.