Add to ORKGTranslating text from diverse sources poses a challenge to current machine translation systems which are rarely adapted to structure beyond corpus level. We explore topic adaptation on a diverse data set and present a new bilingual vari-ant of Latent Dirichlet Allocation to com-pute topic-adapted, probabilistic phrase translation features. We dynamically in-fer document-specific translation proba-bilities for test sets of unknown origin, thereby capturing the effects of document context on phrase translations. We show gains of up to 1.26 BLEU over the base-line and 1.04 over a domain adaptation benchmark. We further provide an anal-ysis of the domain-specific data and show additive gains of our model in combination with other types of topic...
Topic models, an unsupervised technique for inferring translation domains improve machine translatio...
Topic models, an unsupervised technique for inferring translation domains improve machine translatio...
A topic model outputs a set of multinomial distributions over words for each topic. In this pap...
Translating text from diverse sources poses a challenge to current machine translation systems which...
In recent years there has been an increased interest in domain adaptation techniques for statistical...
We propose an approach that biases machine translation systems toward relevant transla-tions based o...
Language models (LMs) are used in Statistical Machine Translation (SMT) to improve the fluency of tr...
In recent years there has been an increased interest in domain adaptation techniques for statistica...
Despite its potential to improve lexical selection, most state-of-the-art machine translation system...
This work presents a simplified approach to bilingual topic modeling for language model adaptation b...
We present a novel topic mixture-based language model adaptation approach that uses La-tent Dirichle...
We propose an unsupervised dynamic language model (LM) adaptation framework using long-distance late...
This paper introduces a selection-based LM using topic modeling for the purpose of domain adaptation...
Lexical selection is crucial for statistical ma-chine translation. Previous studies separately explo...
Topic models, an unsupervised technique for inferring translation domains improve machine translatio...
Topic models, an unsupervised technique for inferring translation domains improve machine translatio...
Topic models, an unsupervised technique for inferring translation domains improve machine translatio...
A topic model outputs a set of multinomial distributions over words for each topic. In this pap...
Translating text from diverse sources poses a challenge to current machine translation systems which...
In recent years there has been an increased interest in domain adaptation techniques for statistical...
We propose an approach that biases machine translation systems toward relevant transla-tions based o...
Language models (LMs) are used in Statistical Machine Translation (SMT) to improve the fluency of tr...
In recent years there has been an increased interest in domain adaptation techniques for statistica...
Despite its potential to improve lexical selection, most state-of-the-art machine translation system...
This work presents a simplified approach to bilingual topic modeling for language model adaptation b...
We present a novel topic mixture-based language model adaptation approach that uses La-tent Dirichle...
We propose an unsupervised dynamic language model (LM) adaptation framework using long-distance late...
This paper introduces a selection-based LM using topic modeling for the purpose of domain adaptation...
Lexical selection is crucial for statistical ma-chine translation. Previous studies separately explo...
Topic models, an unsupervised technique for inferring translation domains improve machine translatio...
Topic models, an unsupervised technique for inferring translation domains improve machine translatio...
Topic models, an unsupervised technique for inferring translation domains improve machine translatio...
A topic model outputs a set of multinomial distributions over words for each topic. In this pap...