Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A phrase-based, joint probability model for statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
New developments in parsing technology
Beam sampling for the infinite hidden Markov model
Proceedings of the 25th international conference on Machine learning
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Sampling alignment structure under a Bayesian translation model
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Inversion transduction grammar for joint phrasal translation modeling
SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
A Gibbs sampler for phrasal synchronous grammar induction
ACL '09 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: Volume 2 - Volume 2
An unsupervised model for joint phrase alignment and extraction
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Machine translation without words through substring alignment
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Substring-based machine translation
Machine Translation
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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This paper describes an efficient sampler for synchronous grammar induction under a non-parametric Bayesian prior. Inspired by ideas from slice sampling, our sampler is able to draw samples from the posterior distributions of models for which the standard dynamic programing based sampler proves intractable on non-trivial corpora. We compare our sampler to a previously proposed Gibbs sampler and demonstrate strong improvements in terms of both training log-likelihood and performance on an end-to-end translation evaluation.