The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
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A phrase-based, joint probability model for statistical machine translation
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Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
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Aligning words using matrix factorisation
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A localized prediction model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Improving a statistical MT system with automatically learned rewrite patterns
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Local phrase reordering models for statistical machine translation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Translating with non-contiguous phrases
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
N-gram-based Machine Translation
Computational Linguistics
A block bigram prediction model for statistical machine translation
ACM Transactions on Speech and Language Processing (TSLP)
A unigram orientation model for statistical machine translation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
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In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models. The units of translation are blocks - pairs of phrases. During decoding, we use a block unigram model and a word-based trigram language model. During training, the blocks are learned from source interval projections using an underlying word alignment. We show experimental results on block selection criteria based on unigram counts and phrase length.