01634nas a2200241   4500000000100000000000100001000000100002008004100003260000800044653002300052653002400075653003100099653001700130653002400147653001900171100002400190700001700214245008900231856015400320300000900474520088900483022002001372       2020                            d  cdec10aImmaterial culture10aMachine translation10aNeural machine translation10aPost-editese10aTranslation quality10aTranslationese1 aPilar Sanchez-Gijon1 aRamon Huerta00aConseqüències de la traducció automàtica neuronal sobre les llengües d arribada  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85101003665&doi=10.5565%2fREV%2fTRADUMATICA.277&partnerID=40&md5=1c6c64970e6c0e1c3da5ddb974754e3b  a1-103 aThis article deals with the consequences of using a neural machine translation trained from corpora compiled from translations of specific text genres produced in particular professional contexts. Phenomena such as translationese and post-editese, associated with the use of machine translation, underline the need to orient research towards new approaches to the question of professional translation quality. Working from the viewpoint that languages should be safeguarded as intangible cultural heritage, it poses a range of questions related to the impact of machine translation on target cultures and languages: how it is perceived, quality and how its use can alter the standard language. It points out the need to define translation quality thresholds that go beyond the aspects most commonly related to error detection, which is the most economically viable type of correction.  a15787559 (ISSN)