@inproceedings{lewis-etal-2010-achieving,
title = "Achieving Domain Specificity in {SMT} without Overt Siloing",
author = "Lewis, William D. and
Wendt, Chris and
Bullock, David",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/791_Paper.pdf",
abstract = "We examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entity. By pooling all available data, building large SMT engines, and using domain-specific target language models, we see boosts in quality, and can achieve the generalizability and resiliency of a larger SMT but with the precision of a domain-specific engine.",
}
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<abstract>We examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entity. By pooling all available data, building large SMT engines, and using domain-specific target language models, we see boosts in quality, and can achieve the generalizability and resiliency of a larger SMT but with the precision of a domain-specific engine.</abstract>
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%0 Conference Proceedings
%T Achieving Domain Specificity in SMT without Overt Siloing
%A Lewis, William D.
%A Wendt, Chris
%A Bullock, David
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F lewis-etal-2010-achieving
%X We examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entity. By pooling all available data, building large SMT engines, and using domain-specific target language models, we see boosts in quality, and can achieve the generalizability and resiliency of a larger SMT but with the precision of a domain-specific engine.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/791_Paper.pdf
Markdown (Informal)
[Achieving Domain Specificity in SMT without Overt Siloing](http://www.lrec-conf.org/proceedings/lrec2010/pdf/791_Paper.pdf) (Lewis et al., LREC 2010)
ACL