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Discriminative training and maximum entropy models for statistical machine translation

Open access
Och, Franz Josef, Ney, Hermann·2001·Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02

Abstract

We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source-channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible hidden variables. This approach allows a baseline machine translation system to be extended easily by adding new feature functions. We show that a baseline statistical machine translation system is significantly improved using this approach.

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Metadata

Authors
Och, F. J., & Ney, H.
Venue
Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02
Published
2001
Pages
295
OpenAlex
W2154124206
Cited by
1,034
References
2
FWCI
11.62
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Discriminative training and maximum entropy models for statistical machine translation. https://app.cochat.ai/papers/10.3115/1073083.1073133
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Och, F. J., & Ney, H. (2001). Discriminative training and maximum entropy models for statistical machine translation. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02. https://doi.org/10.3115/1073083.1073133

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.