The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Decoding complexity in word-replacement translation models
Computational Linguistics
Decoding algorithm in statistical machine translation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Modeling with structures in statistical machine translation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Fast and optimal decoding for machine translation
Artificial Intelligence
Word re-ordering and DP-based search in statistical machine translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
An efficient A* search algorithm for statistical machine translation
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
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
An algorithmic framework for the decoding problem in statistical machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Computing optimal alignments for the IBM-3 translation model
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
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Viterbi Alignment and Decoding are two fundamental search problems in Statistical Machine Translation. Both the problems are known to be NP-hard and therefore, it is unlikely that there exists an optimal polynomial time algorithm for either of these search problems. In this paper we characterize exponentially large subspaces in the solution space of Viterbi Alignment and Decoding. Each of these subspaces admits polynomial time optimal search algorithms. We propose a local search heuristic using a neighbourhood relation on these subspaces. Experimental results show that our algorithms produce better solutions taking substantially less time than the previously known algorithms for these problems.