LEADER 02610nam 2200373 450 001 9910676685003321 005 20230509205647.0 035 $a(CKB)5670000000618211 035 $a(NjHacI)995670000000618211 035 $a(EXLCZ)995670000000618211 100 $a20230509d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine translation for everyone $eempowering users in the age of artificial intelligence /$fedited by Dorothy Kenny 210 1$aBerlin :$cLanguage Science Press,$d2022. 215 $a1 online resource (210 pages) $cillustrations 225 1 $aTranslation and multilingual natural language processing 311 $a3-9855404-5-4 320 $aIncludes bibliographical references and index. 330 $aLanguage learning and translation have always been complementary pillars of multilingualism in the European Union. Both have been affected by the increasing availability of machine translation (MT): language learners now make use of free online MT to help them both understand and produce texts in a second language, but there are fears that uninformed use of the technology could undermine effective language learning. At the same time, MT is promoted as a technology that will change the face of professional translation, but the technical opacity of contemporary approaches, and the legal and ethical issues they raise, can make the participation of human translators in contemporary MT workflows particularly complicated. Against this background, this book attempts to promote teaching and learning about MT among a broad range of readers, including language learners, language teachers, trainee translators, translation teachers, and professional translators. It presents a rationale for learning about MT, and provides both a basic introduction to contemporary machine-learning based MT, and a more advanced discussion of neural MT. It explores the ethical issues that increased use of MT raises, and provides advice on its application in language learning. It also shows how users can make the most of MT through pre-editing, post-editing and customization of the technology. 410 0$aTranslation and multilingual natural language processing. 606 $aMachine translating$vCongresses 615 0$aMachine translating 676 $a418.0 702 $aKenny$b Dorothy 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910676685003321 996 $aMachine translation for everyone$93062088 997 $aUNINA