Human versus Neural Machine Translation Creativity: A Study on Manipulated MWEs in Literature

Abstract

In the digital era, the (r)evolution of neural machine translation (NMT) has reshaped both the market and translators’ workflow. However, the adoption of this technology has not fully reached the creative field of literary translation. Against this background, this study aims to explore to what extent NMT systems can be used to translate the creative challenges posed by idioms, specifically manipulated multiword expressions (MWEs) found in literary texts. To carry out this pilot study, five manipulated MWEs were selected from a fantasy novel and machine-translated (English > Spanish) by four NMT systems (DeepL, Google Translate, Bing Translator, and Reverso). Then, each NMT output as well as a human translation are assessed by six professional literary translators by using a human evaluation sheet. Based on these results, the creativity obtained in each translation method was calculated. Despite the satisfactory performance of both DeepL and Google Translate, HT creativity was highly superior in almost all manipulated MWEs. To the best of our knowledge, this paper not only contributes to the ongoing study of NMT applied to literature, but it is also one of the few studies that delve into the almost unexplored field of assessing creativity in neural machine-translated MWEs.