A systematic comparison of various statistical alignment models
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Statistical phrase-based translation
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A phrase-based, joint probability model for statistical machine translation
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HMM word and phrase alignment for statistical machine translation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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Hierarchical Phrase-Based Translation
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Generative models of monolingual and bilingual gappy patterns
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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We propose a principled and efficient phrase-to-phrase alignment model, useful in machine translation as well as other related natural language processing problems. In a hidden semi-Markov model, word-to-phrase and phrase-to-word translations are modeled directly by the system. Agreement between two directional models encourages the selection of parsimonious phrasal alignments, avoiding the overfitting commonly encountered in unsupervised training with multi-word units. Expanding the state space to include "gappy phrases" (such as French ne * pas) makes the alignment space more symmetric; thus, it allows agreement between discontinuous alignments. The resulting system shows substantial improvements in both alignment quality and translation quality over word-based Hidden Markov Models, while maintaining asymptotically equivalent runtime.