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    <abstract>Statistical Machine Translation (SMT) systems specialized for one domain often perform poorly when applied to other domains. Domain adaptation techniques allow SMT models trained from a source domain with abundant data to accommodate different target domains with limited data. This paper evaluates the performance of two adaptive techniques based on log-linear and mixture models on data from the legal domain in real-world settings. Performance evaluation includes post-editing time and effort required by a professional post-editor to improve the quality of machine-generated translations to meet industry standards, as well as traditional automated scoring techniques (BLEU scores). Results indicates that the domain adaptation techniques can yield a significant increase in BLEU score (up to three points) and a significant reduction in post-editing time of about one second per word in an operational environment.</abstract>
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%0 Conference Proceedings
%T Evaluation of Domain Adaptation Techniques for TRANSLI in a Real-World Environment
%A Farzindar, Atefeh
%A Khreich, Wael
%S Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program
%D 2012
%8 oct 28 nov 1
%I Association for Machine Translation in the Americas
%C San Diego, California, USA
%F farzindar-khreich-2012-evaluation
%X Statistical Machine Translation (SMT) systems specialized for one domain often perform poorly when applied to other domains. Domain adaptation techniques allow SMT models trained from a source domain with abundant data to accommodate different target domains with limited data. This paper evaluates the performance of two adaptive techniques based on log-linear and mixture models on data from the legal domain in real-world settings. Performance evaluation includes post-editing time and effort required by a professional post-editor to improve the quality of machine-generated translations to meet industry standards, as well as traditional automated scoring techniques (BLEU scores). Results indicates that the domain adaptation techniques can yield a significant increase in BLEU score (up to three points) and a significant reduction in post-editing time of about one second per word in an operational environment.
%U https://aclanthology.org/2012.amta-commercial.6

Markdown (Informal)

[Evaluation of Domain Adaptation Techniques for TRANSLI in a Real-World Environment](https://aclanthology.org/2012.amta-commercial.6) (Farzindar & Khreich, AMTA 2012)

• Evaluation of Domain Adaptation Techniques for TRANSLI in a Real-World Environment (Farzindar & Khreich, AMTA 2012)

ACL

• Atefeh Farzindar and Wael Khreich. 2012. Evaluation of Domain Adaptation Techniques for TRANSLI in a Real-World Environment. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program, San Diego, California, USA. Association for Machine Translation in the Americas.