The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
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NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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SSST '07 Proceedings of the NAACL-HLT 2007/AMTA Workshop on Syntax and Structure in Statistical Translation
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COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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Probabilistic word alignment under the L0-norm
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Fast inference in phrase extraction models with belief propagation
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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The joint probability model proposed by Marcu and Wong (2002) provides a strong probabilistic framework for phrase-based statistical machine translation (SMT). The model's usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present the first model to use word alignments for constraining the space of phrasal alignments searched during Expectation Maximization (EM) training. Constraining the joint model improves performance, showing results that are very close to state-of-the-art phrase-based models. It also allows it to scale up to larger corpora and therefore be more widely applicable.