Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Maximum entropy based phrase reordering model for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Tree-to-string alignment template for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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
Improving alignments for better confusion networks for combining machine translation systems
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Indirect-HMM-based hypothesis alignment for combining outputs from machine translation systems
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lattice Minimum Bayes-Risk decoding for statistical machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
The feature subspace method for SMT system combination
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Model combination for machine translation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Boosting-based system combination for machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Translation model generalization using probability averaging for machine translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Mixture model-based minimum Bayes risk decoding using multiple machine translation systems
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Heterogeneous parsing via collaborative decoding
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Hybrid decoding: decoding with partial hypotheses combination over multiple SMT systems
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Engkoo: mining the web for language learning
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Hypothesis mixture decoding for statistical machine translation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Learning translation consensus with structured label propagation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Bagging and Boosting statistical machine translation systems
Artificial Intelligence
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This paper presents collaborative decoding (co-decoding), a new method to improve machine translation accuracy by leveraging translation consensus between multiple machine translation decoders. Different from system combination and MBR decoding, which post-process the n-best lists or word lattice of machine translation decoders, in our method multiple machine translation decoders collaborate by exchanging partial translation results. Using an iterative decoding approach, n-gram agreement statistics between translations of multiple decoders are employed to re-rank both full and partial hypothesis explored in decoding. Experimental results on data sets for NIST Chinese-to-English machine translation task show that the co-decoding method can bring significant improvements to all baseline decoders, and the outputs from co-decoding can be used to further improve the result of system combination.