The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A systematic comparison of various statistical alignment models
Computational Linguistics
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
Improving alignments for better confusion networks for combining machine translation systems
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
SPMT: statistical machine translation with syntactified target language phrases
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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
Are very large n-best lists useful for SMT?
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Sentence level machine translation evaluation as a ranking problem: one step aside from BLEU
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Collaborative decoding: partial hypothesis re-ranking using translation consensus between decoders
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
Boosting-based system combination for machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Bagging and Boosting statistical machine translation systems
Artificial Intelligence
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Recently system combination has been shown to be an effective way to improve translation quality over single machine translation systems. In this paper, we present a simple and effective method to systematically derive an ensemble of SMT systems from one baseline linear SMT model for use in system combination. Each system in the resulting ensemble is based on a feature set derived from the features of the baseline model (typically a subset of it). We will discuss the principles to determine the feature sets for derived systems, and present in detail the system combination model used in our work. Evaluation is performed on the data sets for NIST 2004 and NIST 2005 Chinese-to-English machine translation tasks. Experimental results show that our method can bring significant improvements to baseline systems with state-of-the-art performance.