Creativity in translation: machine translation as a constraint for literary texts

TL;DR

This study compares different translation modalities for literary texts, revealing that human translation and post-editing foster more creativity than neural machine translation, which tends to produce literal and less creative outputs.

Contribution

It provides a quantitative analysis of creativity in translation, highlighting the limitations of neural machine translation in producing creative literary translations.

Findings

Human translation scores highest in creativity.

Neural machine translation produces literal, less creative results.

Post-editing constrains translator creativity.

Abstract

This article presents the results of a study involving the translation of a short story by Kurt Vonnegut from English to Catalan and Dutch using three modalities: machine-translation (MT), post-editing (PE) and translation without aid (HT). Our aim is to explore creativity, understood to involve novelty and acceptability, from a quantitative perspective. The results show that HT has the highest creativity score, followed by PE, and lastly, MT, and this is unanimous from all reviewers. A neural MT system trained on literary data does not currently have the necessary capabilities for a creative translation; it renders literal solutions to translation problems. More importantly, using MT to post-edit raw output constrains the creativity of translators, resulting in a poorer translation often not fit for publication, according to experts.