Discriminative training and maximum entropy models for statistical machine translation

被引：0

作者：

Och, FJ ^{[1
]}

Ney, H ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Rhein Westfal TH Aachen, Dept Comp Sci, Lehrstuhl Informat 6, D-52056 Aachen, Germany

来源：

40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE | 2002年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：295 / 302

页数：8

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