Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Decoding complexity in word-replacement translation models
Computational Linguistics
The RWTH system for statistical translation of spoken dialogues
HLT '01 Proceedings of the first international conference on Human language technology research
The Candide system for machine translation
HLT '94 Proceedings of the workshop on Human Language Technology
A corpus-centered approach to spoken language translation
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
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This paper gives an overview of the stochastic modelling approach to machine translation. Starting with the Bayes decision rule as in pattern classification and speech recognition, we show how the resulting system architecture can be structured into three parts: the language model probability, the string translation model probability and the search procedure that generates the word sequence in the target language. We discuss the properties of the system components and report results on the translation of spoken dialogues in the VERBMOBIL project. The experience obtained in the VERBMOBIL project, in particular a large-scale end-to-end evaluation, showed that the stochastic modelling approach resulted in significantly lower error rates than three competing translation approaches: the sentence error rate was 29% in comparison with 52% to 62% for the other translation approaches.