@inproceedings{corston-oliver-gamon-2003-combining,
title = "Combining decision trees and transformation-based learning to correct transferred linguistic representations",
author = "Corston-Oliver, Simon and
Gamon, Michael",
booktitle = "Proceedings of Machine Translation Summit IX: Papers",
month = sep # " 23-27",
year = "2003",
address = "New Orleans, USA",
url = "https://aclanthology.org/2003.mtsummit-papers.8/",
abstract = "We approach to correcting features in transferred linguistic representations in machine translation. The hybrid approach combines decision trees and transformation-based learning. Decision trees serve as a filter on the intractably large search space of possible interrelations among features. Transformation-based learning results in a simple set of ordered rules that can be compiled and executed after transfer and before sentence realization in the target language. We measure the reduction in noise in the linguistic representations and the results of human evaluations of end-to-end English-German machine translation."
}
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<abstract>We approach to correcting features in transferred linguistic representations in machine translation. The hybrid approach combines decision trees and transformation-based learning. Decision trees serve as a filter on the intractably large search space of possible interrelations among features. Transformation-based learning results in a simple set of ordered rules that can be compiled and executed after transfer and before sentence realization in the target language. We measure the reduction in noise in the linguistic representations and the results of human evaluations of end-to-end English-German machine translation.</abstract>
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%0 Conference Proceedings
%T Combining decision trees and transformation-based learning to correct transferred linguistic representations
%A Corston-Oliver, Simon
%A Gamon, Michael
%S Proceedings of Machine Translation Summit IX: Papers
%D 2003
%8 sep 23 27
%C New Orleans, USA
%F corston-oliver-gamon-2003-combining
%X We approach to correcting features in transferred linguistic representations in machine translation. The hybrid approach combines decision trees and transformation-based learning. Decision trees serve as a filter on the intractably large search space of possible interrelations among features. Transformation-based learning results in a simple set of ordered rules that can be compiled and executed after transfer and before sentence realization in the target language. We measure the reduction in noise in the linguistic representations and the results of human evaluations of end-to-end English-German machine translation.
%U https://aclanthology.org/2003.mtsummit-papers.8/
Markdown (Informal)
[Combining decision trees and transformation-based learning to correct transferred linguistic representations](https://aclanthology.org/2003.mtsummit-papers.8/) (Corston-Oliver & Gamon, MTSummit 2003)
ACL