Gutenberg goes neural : comparing features of Dutch human translations with raw neural machine translation outputs in a corpus of English literary classics
Due to the growing success of neural machine translation (NMT), many have started to question its applicability within the field of literary translation. In order to grasp the possibilities of NMT, we studied the output of the neural machine system of Google Translate (GNMT) and DeepL when applied to four classic novels translated from English into Dutch. The quality of the NMT systems is discussed by focusing on manual annotations, and we also employed various metrics in order to get an insight into lexical richness, local cohesion, syntactic, and stylistic difference. Firstly, we discovered... Mehr ...
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Dokumenttyp: | journalarticle |
Erscheinungsdatum: | 2020 |
Schlagwörter: | Languages and Literatures / LT3 / literary machine translation / neural machine translation / quality assessment / lexical richness / cohesion / syntactic divergence / Burrows’ delta |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-27450909 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://biblio.ugent.be/publication/8673762 |