© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer Reviewe
A data-driven approach to model translation suffers from the data mismatch problem and demands domai...
Abstract tra Statistical machine translation systems are usually trained on large amounts of bilingu...
Statistical machine translation (SMT) systems use statistical learning methods to learn how to trans...
© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given t...
Globalization suddenly brings many people from different country to interact with each other, requir...
Abstract. Statistical Machine Translation (SMT) systems are usually trained on large amounts of bili...
In this paper, we tackle the problem of domain adaptation of statistical machine translation (SMT) b...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
The Author(s) 2015. This article is published with open access at Springerlink.com Abstract In this ...
In this thesis we develop and evaluate a general framework for domain-adaptation of statistical mach...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
We tackle the problem of domain adapta-tion of Statistical Machine Translation by exploiting domain-...
Differences in domains of language use between training data and test data have often been reported ...
Abstract. Statistical Machine Translation (SMT) is currently used in real-time and commercial settin...
This paper reports on the ongoing work focused on domain adaptation of statistical machine translati...
A data-driven approach to model translation suffers from the data mismatch problem and demands domai...
Abstract tra Statistical machine translation systems are usually trained on large amounts of bilingu...
Statistical machine translation (SMT) systems use statistical learning methods to learn how to trans...
© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given t...
Globalization suddenly brings many people from different country to interact with each other, requir...
Abstract. Statistical Machine Translation (SMT) systems are usually trained on large amounts of bili...
In this paper, we tackle the problem of domain adaptation of statistical machine translation (SMT) b...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
The Author(s) 2015. This article is published with open access at Springerlink.com Abstract In this ...
In this thesis we develop and evaluate a general framework for domain-adaptation of statistical mach...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
We tackle the problem of domain adapta-tion of Statistical Machine Translation by exploiting domain-...
Differences in domains of language use between training data and test data have often been reported ...
Abstract. Statistical Machine Translation (SMT) is currently used in real-time and commercial settin...
This paper reports on the ongoing work focused on domain adaptation of statistical machine translati...
A data-driven approach to model translation suffers from the data mismatch problem and demands domai...
Abstract tra Statistical machine translation systems are usually trained on large amounts of bilingu...
Statistical machine translation (SMT) systems use statistical learning methods to learn how to trans...