A systematic comparison of various statistical alignment models
Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Machine translation system combination using ITG-based alignments
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
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
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Sequentially finding the N-best list in hidden Markov models
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Joint optimization for machine translation system combination
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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
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
A decoding method of system combination based on hypergraph in SMT
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Review of hypothesis alignment algorithms for MT system combination via confusion network decoding
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
Bagging and Boosting statistical machine translation systems
Artificial Intelligence
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Inspired by the incremental TER alignment, we re-designed the Indirect HMM (IHMM) alignment, which is one of the best hypothesis alignment methods for conventional MT system combination, in an incremental manner. One crucial problem of incremental alignment is to align a hypothesis to a confusion network (CN). Our incremental IHMM alignment is implemented in three different ways: 1) treat CN spans as HMM states and define state transition as distortion over covered n-grams between two spans; 2) treat CN spans as HMM states and define state transition as distortion over words in component translations in the CN; and 3) use a consensus decoding algorithm over one hypothesis and multiple IHMMs, each of which corresponds to a component translation in the CN. All these three approaches of incremental alignment based on IHMM are shown to be superior to both incremental TER alignment and conventional IHMM alignment in the setting of the Chinese-to-English track of the 2008 NIST Open MT evaluation.