Monte Carlo techniques for phrase-based translation

Machine Translation

Volumn 24, Issue 2, 2010, Pages 103-121

  Arun, Abhishek ^a   Haddow, Barry ^a   Koehn, Philipp ^a   Lopez, Adam ^a   Dyer, Chris ^b   Blunsom, Phil ^c  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b UNIVERSITY OF MARYLAND   (United States)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Gibbs sampling; Machine learning; MCMC; Statistical machine translation

Indexed keywords

ALTERNATIVE APPROACH; BEAM SEARCH; GIBBS SAMPLERS; GIBBS SAMPLING; INFERENCE PROBLEM; MACHINE LEARNING; MAXIMUM PROBABILITY; MCMC; MONTE CARLO TECHNIQUES; NP COMPLETE; POSTERIOR DISTRIBUTIONS; STATISTICAL MACHINE TRANSLATION; TRANSLATION MODELS;

INFORMATION THEORY; LEARNING SYSTEMS; LINGUISTICS; MONTE CARLO METHODS;

SAMPLING;

EID: 78650170571     PISSN: 09226567     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10590-010-9080-7     Document Type: Article

References (25)

1. Arun A, Dyer C, Haddow B, Blunsom P, Lopez A, Koehn P (2009) Monte Carlo inference and maximization for phrase-based translation. In: Proceedings of CoNLL, Association for Computational Linguistics, Boulder, Colorado, pp 102-110
2. Blunsom P, Cohn T, Osborne M (2008) A discriminative latent variable model for statistical machine translation. In: Proceedings of ACL-08: HLT, Association for Computational Linguistics, Columbus, Ohio, pp 200-208
3. Callison-Burch C, Koehn P, Monz C, Schroeder J (2009) Findings of the 2009 workshop on statistical machine translation. In: Proceedings of the fourth workshop on statistical machine translation, Association for Computational Linguistics, Athens, Greece, pp 1-28
4. Casacuberta F, Higuera CDL (2000) Computational complexity of problems on probabilistic grammars and transducers. Springer-Verlag, London, UK
5. DeNero J, Bouchard-Côté A, Klein D (2008) Sampling alignment structure under a Bayesian translation model. In: Proceedings of the 2008 conference on empirical methods in natural language processing, Association for Computational Linguistics, Honolulu, Hawaii, pp 314-323
6. Eisner J, Tromble RW (2006) Local search with very large-scale neighborhoods for optimal permutations in machine translation. In: Proceedings of the HLT-NAACL workshop on computationally hard problems and joint inference in speech and language processing, New York, pp 57-75
7. Finkel JR, Manning CD, Ng AY (2006) Solving the problem of cascading errors: approximate bayesian inference for linguistic annotation pipelines. In: Proceedings of the 2006 conference on empirical methods in natural language processing, Association for Computational Linguistics, Sydney, Australia, pp 618-626
8. S Geman D Geman 1984 Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images IEEE Trans Pattern Anal Mach Intell 6 721 741 0573.62030 10.1109/TPAMI.1984.4767596
9. Germann U, Jahr M, Knight K, Marcu D, Yamada K (2001) Fast decoding and optimal decoding for machine translation. In: Proceedings of 39th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Toulouse, France, pp 228-235
10. PJ Green 1995 Reversible jump Markov chain Monte Carlo computation and Bayesian model determination Biometrika 82 711 732 0861.62023 10.1093/biomet/82.4.711 1380810
11. Johnson H, Martin J, Foster G, Kuhn R (2007a) Improving translation quality by discarding most of the phrasetable. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), Association for Computational Linguistics, Prague, Czech Republic, pp 967-975
12. Johnson M, Griffiths T, Goldwater S (2007b) Bayesian inference for PCFGs via Markov Chain Monte Carlo. In: Human language technologies 2007: the conference of the North American chapter of the Association for Computational Linguistics, Proceedings of the Main Conference, Association for Computational Linguistics, Rochester, New York, pp 139-146
13. Koehn P, Hoang H (2007) Factored translation models. In: Proceedings of EMNLP, Association for Computational Linguistics, Prague, Czech Republic, pp 868-876
14. Koehn P, Och F, Marcu D (2003) Statistical phrase-based translation. In: Proceedings of HLT-NAACL. Morristown, NJ, USA, pp 48-54
15. Kumar S, Byrne W (2004) Minimum Bayes-risk decoding for statistical machine translation. In: Susan Dumais DM, Roukos S (eds) HLT-NAACL 2004: main proceedings, Association for Computational Linguistics, Boston, Massachusetts, USA, pp 169-176
16. Langlais P, Gotti F, Patry A (2007) A greedy decoder for phrase-based statistical machine translation. In: 11th international conference on theoretical and methodological issues in machine translation (TMI 2007), Skd́cvde, Sweden, pp 104-113
17. Li Z, Eisner J, Khudanpur S (2009) Variational decoding for statistical machine translation. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP, pp 593-601
18. DC Liu J Nocedal 1989 On the limited memory BFGS method for large scale optimization Math Program 45 3 503 528 0696.90048 10.1007/BF01589116 1038245
19. Marcu D, Wong W (2002) A phrase-based, joint probability model for statistical machine translation. In: EMNLP '02: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, Association for Computational Linguistics, Morristown, NJ, USA, pp 133-139
20. N Metropolis S Ulam 1949 The Monte Carlo method J Am Stat Assoc 44 247 335 341 0033.28807 10.2307/2280232 31341
21. Och FJ (2003) Minimum error rate training in statistical machine translation. In: Proceedings of the 41st annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Sapporo, Japan, pp 160-167
22. Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of 40th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, pp 311-318
23. Schraudolph NN (1999) Local gain adaptation in stochastic gradient descent. Technical Report IDSIA-09-99, IDSIA
24. Smith DA, Eisner J (2006) Minimum risk annealing for training log-linear models. In: Proceedings of the COLING/ACL 2006 main conference poster sessions, Sydney, Australia, pp 787-794
25. Zens R, Hasan S, Ney H (2007) A systematic comparison of training criteria for statistical machine translation. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), pp 524-532