Add to ORKGDomain Adaptation in Machine Translation means to take a machine translation system that is restricted to work in a specific context and to enable the system to translate text from a different domain. The paper presents a two-step domain adaptation strategy, by first making use of unlabeled training material through an unsupervised algorithm, the Self-Organizing Map, to create auxiliary language models, and then to include these models dynamically in a machine translation pipelin
While statistical machine translation (SMT) has advanced significantly with better modeling techniqu...
Differences in domains of language use between training data and test data have often been reported ...
Abstract tra Statistical machine translation systems are usually trained on large amounts of bilingu...
This thesis explores the application of unsupervised clustering for domain adaptation of machine tra...
Supervised machine translation works well when the train and test data are sampled from the same dis...
Relying on large-scale parallel corpora, neural machine translation has achieved great success in ce...
Large amounts of bilingual corpora are used in the training process of statistical machine translati...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
Abstract. Statistical Machine Translation (SMT) systems are usually trained on large amounts of bili...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
In this paper, we tackle the problem of domain adaptation of statistical machine translation (SMT) b...
In this work, we tackle the problem of language and translation models domain-adaptation without exp...
In this work, we tackle the problem of language and translation models domain-adaptation without exp...
While statistical machine translation (SMT) has advanced significantly with better modeling techniqu...
Differences in domains of language use between training data and test data have often been reported ...
Abstract tra Statistical machine translation systems are usually trained on large amounts of bilingu...
This thesis explores the application of unsupervised clustering for domain adaptation of machine tra...
Supervised machine translation works well when the train and test data are sampled from the same dis...
Relying on large-scale parallel corpora, neural machine translation has achieved great success in ce...
Large amounts of bilingual corpora are used in the training process of statistical machine translati...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
Abstract. Statistical Machine Translation (SMT) systems are usually trained on large amounts of bili...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
Domain adaptation for machine translation (MT) can be achieved by selecting training instances close...
In this paper, we tackle the problem of domain adaptation of statistical machine translation (SMT) b...
In this work, we tackle the problem of language and translation models domain-adaptation without exp...
In this work, we tackle the problem of language and translation models domain-adaptation without exp...
While statistical machine translation (SMT) has advanced significantly with better modeling techniqu...
Differences in domains of language use between training data and test data have often been reported ...
Abstract tra Statistical machine translation systems are usually trained on large amounts of bilingu...