@inproceedings{wang-etal-2012-improved,
title = "Improved Domain Adaptation for Statistical Machine Translation",
author = "Wang, Wei and
Macherey, Klaus and
Macherey, Wolfgang and
Och, Franz and
Xu, Peng",
booktitle = "Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers",
month = oct # " 28-" # nov # " 1",
year = "2012",
address = "San Diego, California, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2012.amta-papers.18",
abstract = "We present a simple and effective infrastructure for domain adaptation for statistical machine translation (MT). To build MT systems for different domains, it trains, tunes and deploys a single translation system that is capable of producing adapted domain translations and preserving the original generic accuracy at the same time. The approach unifies automatic domain detection and domain model parameterization into one system. Experiment results on 20 language pairs demonstrate its viability.",
}
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<abstract>We present a simple and effective infrastructure for domain adaptation for statistical machine translation (MT). To build MT systems for different domains, it trains, tunes and deploys a single translation system that is capable of producing adapted domain translations and preserving the original generic accuracy at the same time. The approach unifies automatic domain detection and domain model parameterization into one system. Experiment results on 20 language pairs demonstrate its viability.</abstract>
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%0 Conference Proceedings
%T Improved Domain Adaptation for Statistical Machine Translation
%A Wang, Wei
%A Macherey, Klaus
%A Macherey, Wolfgang
%A Och, Franz
%A Xu, Peng
%S Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2012
%8 oct 28 nov 1
%I Association for Machine Translation in the Americas
%C San Diego, California, USA
%F wang-etal-2012-improved
%X We present a simple and effective infrastructure for domain adaptation for statistical machine translation (MT). To build MT systems for different domains, it trains, tunes and deploys a single translation system that is capable of producing adapted domain translations and preserving the original generic accuracy at the same time. The approach unifies automatic domain detection and domain model parameterization into one system. Experiment results on 20 language pairs demonstrate its viability.
%U https://aclanthology.org/2012.amta-papers.18
Markdown (Informal)
[Improved Domain Adaptation for Statistical Machine Translation](https://aclanthology.org/2012.amta-papers.18) (Wang et al., AMTA 2012)
ACL
- Wei Wang, Klaus Macherey, Wolfgang Macherey, Franz Och, and Peng Xu. 2012. Improved Domain Adaptation for Statistical Machine Translation. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.