@inproceedings{birch-etal-2006-constraining-phrase,
title = "Constraining the Phrase-Based, Joint Probability Statistical Translation Model",
author = "Birch, Alexandra and
Callison-Burch, Chris and
Osborne, Miles",
booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
month = aug # " 8-12",
year = "2006",
address = "Cambridge, Massachusetts, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2006.amta-papers.2",
pages = "10--18",
abstract = "The Joint Probability Model proposed by Marcu and Wong (2002) provides a probabilistic framework for modeling phrase-based statistical machine transla- tion (SMT). The model{'}s usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present a method of constraining the search space of the Joint Probability Model based on statistically and linguistically motivated word align- ments. This method reduces the complexity and size of the Joint Model and allows it to display performance superior to the standard phrase-based models for small amounts of training material.",
}
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<abstract>The Joint Probability Model proposed by Marcu and Wong (2002) provides a probabilistic framework for modeling phrase-based statistical machine transla- tion (SMT). The model’s usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present a method of constraining the search space of the Joint Probability Model based on statistically and linguistically motivated word align- ments. This method reduces the complexity and size of the Joint Model and allows it to display performance superior to the standard phrase-based models for small amounts of training material.</abstract>
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%0 Conference Proceedings
%T Constraining the Phrase-Based, Joint Probability Statistical Translation Model
%A Birch, Alexandra
%A Callison-Burch, Chris
%A Osborne, Miles
%S Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers
%D 2006
%8 aug 8 12
%I Association for Machine Translation in the Americas
%C Cambridge, Massachusetts, USA
%F birch-etal-2006-constraining-phrase
%X The Joint Probability Model proposed by Marcu and Wong (2002) provides a probabilistic framework for modeling phrase-based statistical machine transla- tion (SMT). The model’s usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present a method of constraining the search space of the Joint Probability Model based on statistically and linguistically motivated word align- ments. This method reduces the complexity and size of the Joint Model and allows it to display performance superior to the standard phrase-based models for small amounts of training material.
%U https://aclanthology.org/2006.amta-papers.2
%P 10-18
Markdown (Informal)
[Constraining the Phrase-Based, Joint Probability Statistical Translation Model](https://aclanthology.org/2006.amta-papers.2) (Birch et al., AMTA 2006)
ACL