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Maximum entropy model learning of the translation rules

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ACL '98/COLING '98: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 2

Pages 1171 - 1175

https://doi.org/10.3115/980691.980760

Published: 10 August 1998 Publication History

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Abstract

This paper proposes a learning method of translation rules from parallel corpora. This method applies the maximum entropy principle to a probabilistic model of translation rules. First, we define feature functions which express statistical properties of this model. Next, in order to optimize the model, the system iterates following steps: (1) selects a feature function which maximizes loglikelihood, and (2) adds this function to the model incrementally. As computational cost associated with this model is too expensive, we propose several methods to suppress the overhead in order to realize the system. The result shows that it attained 69.54% recall rate.

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Sato KSaito H(2002)Extracting word sequence correspondences with support vector machinesProceedings of the 19th international conference on Computational linguistics - Volume 110.3115/1072228.1072248(1-7)Online publication date: 24-Aug-2002
https://dl.acm.org/doi/10.3115/1072228.1072248
Kim SYoon JSong MKay M(2000)Structural feature selection for English-Korean statistical machine translationProceedings of the 18th conference on Computational linguistics - Volume 110.3115/990820.990884(439-445)Online publication date: 31-Jul-2000
https://dl.acm.org/doi/10.3115/990820.990884

Maximum entropy model learning of the translation rules
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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ACL '98/COLING '98: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 2

August 1998

768 pages

Program Chairs:
Christian Boitet
Université Joseph Fourier, Grenoble, France
,
Pete Whitelock
Sharp Laboratories of Europe Ltd., Oxford, United Kingdom

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Association for Computational Linguistics

United States

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Published: 10 August 1998

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Sato KSaito H(2002)Extracting word sequence correspondences with support vector machinesProceedings of the 19th international conference on Computational linguistics - Volume 110.3115/1072228.1072248(1-7)Online publication date: 24-Aug-2002
https://dl.acm.org/doi/10.3115/1072228.1072248
Kim SYoon JSong MKay M(2000)Structural feature selection for English-Korean statistical machine translationProceedings of the 18th conference on Computational linguistics - Volume 110.3115/990820.990884(439-445)Online publication date: 31-Jul-2000
https://dl.acm.org/doi/10.3115/990820.990884

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Maximum entropy based phrase reordering model for statistical machine translation

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Maximum entropy based rule selection model for syntax-based statistical machine translation

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Maximum entropy based phrase reordering model for statistical machine translation

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