Regular models of phonological rule systems
Computational Linguistics - Special issue on computational phonology
Efficient string matching: an aid to bibliographic search
Communications of the ACM
Automata: Theoretic Aspects of Formal Power Series
Automata: Theoretic Aspects of Formal Power Series
Text classification using string kernels
The Journal of Machine Learning Research
An efficient compiler for weighted rewrite rules
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Rational Kernels: Theory and Algorithms
The Journal of Machine Learning Research
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
Lattice Minimum Bayes-Risk decoding for statistical machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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 1 - Volume 1
Fast consensus decoding over translation forests
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
Variational decoding for statistical machine translation
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
OpenFst: a general and efficient weighted finite-state transducer library
CIAA'07 Proceedings of the 12th international conference on Implementation and application of automata
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Lattice BLEU oracles in machine translation
ACM Transactions on Speech and Language Processing (TSLP)
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This paper presents efficient algorithms for expected similarity maximization, which coincides with minimum Bayes decoding for a similarity-based loss function. Our algorithms are designed for similarity functions that are sequence kernels in a general class of positive definite symmetric kernels. We discuss both a general algorithm and a more efficient algorithm applicable in a common unambiguous scenario. We also describe the application of our algorithms to machine translation and report the results of experiments with several translation data sets which demonstrate a substantial speed-up. In particular, our results show a speed-up by two orders of magnitude with respect to the original method of Tromble et al. (2008) and by a factor of 3 or more even with respect to an approximate algorithm specifically designed for that task. These results open the path for the exploration of more appropriate or optimal kernels for the specific tasks considered.