{"id":3308,"date":"2017-09-04T09:36:31","date_gmt":"2017-09-04T07:36:31","guid":{"rendered":"https:\/\/www-staging.systransoft.com\/?post_type=paper&#038;p=3308"},"modified":"2023-11-16T18:00:53","modified_gmt":"2023-11-16T17:00:53","slug":"domain-control-for-neural-machine-translation-pdf","status":"publish","type":"paper","link":"https:\/\/www-staging.systransoft.com\/de\/ressourcen\/papers-and-publications\/domain-control-for-neural-machine-translation-pdf\/","title":{"rendered":"Domain Control for Neural Machine Translation [PDF]"},"content":{"rendered":"<p>Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.<\/p>","protected":false},"excerpt":{"rendered":"<p>Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements &hellip; <a href=\"https:\/\/www-staging.systransoft.com\/de\/ressourcen\/papers-and-publications\/domain-control-for-neural-machine-translation-pdf\/\">anhaltend<\/a><\/p>","protected":false},"featured_media":0,"template":"","class_list":["post-3308","paper","type-paper","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www-staging.systransoft.com\/de\/wp-json\/wp\/v2\/paper\/3308","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www-staging.systransoft.com\/de\/wp-json\/wp\/v2\/paper"}],"about":[{"href":"https:\/\/www-staging.systransoft.com\/de\/wp-json\/wp\/v2\/types\/paper"}],"wp:attachment":[{"href":"https:\/\/www-staging.systransoft.com\/de\/wp-json\/wp\/v2\/media?parent=3308"}],"curies":[{"name":"WP","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}