Impact of translation workflows with and without MT on textual characteristics in literary translation (2024)

Abstract: The use of machine translation is increasingly being explored for the translation of literary texts, but there is still a lot of uncertainty about the optimal translation workflow in these scenarios. While overall quality is quite good, certain textual characteristics can be different in a human translated text and a text produced by means of machine translation post-editing, which has been shown to potentially have an impact on reader perceptions and experience as well. In this study, we look at textual characteristics from short story translations from B.J. Novak’s One more thing into Dutch. Twenty-three professional literary translators translated three short stories, in three different conditions: using Word, using the classic CAT tool Trados, and using a machine translation post-editing platform specifically designed for literary translation. We look at overall text characteristics (sentence length, type-token ratio, stylistic differences) to establish whether translation workflow has an impact on these features, and whether the three workflows lead to very different final translations or not.

## introduction while originally an outrageous or at least unorthodox idea, the concept of using technology and even machine translation for literary texts has gained ground in recent years. this is evidenced by the existence of works specifically dedicated to technology and creative-text translation @xcite , the existence of a literary mt ## related research an important consideration when using mt for literary text translation, is that mt has been shown to lead to a decrease in lexical richness @xcite . even after postediting, the effects of the mt seem to linger, with post-edited texts having lower lexical variety and density than human translations, and having more interference from the source language @xcite . in the context of literary machine translation specifically, research has shown that mt systems (both google translate and deepl) produce texts that are lexically less diverse than human translations, and that they have lower lexical and semantic cohesion @xcite . the authors also calculated stylistic differences using burrows' delta and found that the styles of google translate and deepl were quite similar, whereas the distance between both mt systems and the human translations was much greater @xcite . even in a post-editing context, where a professional literary translator is actively requested to keep his own typical translator style, certain words he normally would avoid are still maintained from the mt suggestions @xcite . subsequent research into reading experiences found that differences between human translations, machine translations and post-edited texts led to differences in narrative engagement, with human translations generally being rated higher @xcite . however, the authors also found some surprising differences between different languages, with catalan readers preferring human translations and dutch readers preferring to read a text in the english source, or the post-edited version, potentially precisely because it remains closer to the source @xcite . these studies suggest that it is crucial to explore textual features of translations produced in different conditions, in order to (in future) explore the influence on translators style and to predict the effects on reading experiences for different kinds of readers. ## methodology the goal of this study was to explore the impact of translation workflow on final text characteristics. different translation workflows were simulated by means of three possible translation conditions: microsoft word without specific translation technology support, trados studio 2022 with a relevant translation memory and termbase, and a proprietary mtpe platform. the main textual features we wanted to explore were: we hypothesized that the more technologydriven workflows would lead to lexically less diverse translations across participants (guerberof-arenas & toral, 2024; @xcite . ## discussion and conclusions the main goal of this exploratory study was to establish the impact of translation workflow on textual differences. we compared the translations produced by professional literary translators in three conditions: using microsoft word, using trados, and using a proprietary mtpe tool. the hypothesis was that translations produced in word would showcase the most individuality and divergence from the source text as the blank page does not offer specific constraints, whereas the mtpe was expected to remain closest to source and/or mt output as it offers the mt output as a starting point. trados was expected to lead to some constraints (particularly by forcing translators to work on a segment level), but fewer than the mtpe workflow (as there was no translation to start from here). we compared average sentence length and changes in sentence alignment, lexical diversity, and stylometric differences. average sentence length did not seem to differ remarkably across conditions. earlier research on sentence patterns in english and dutch showed that, in contrast with academic texts, newspaper articles, and leaflets, sentence length for short stories can actually be similar in both languages @xcite . the present study shows that the condition in which the text was produced does not change this. changes in sentence alignment were also relatively rare, and occurred in all three conditions, contrary to expectations that they would be most frequent in the word condition. based on previous research, we expected mtpe to be less lexically diverse than translations in other conditions, but this could not be confirmed either (on the contrary, mtpe was the most lexically diverse for 2/3 texts). stylometric analysis based on burrow's delta (2002) did show some similarities between mt output and a majority of translations produced in the mtpe condition, indicating that there is some similarity in their word use. the analysis presented in this paper is a preliminary analysis of textual features in our dataset that contradicts some core assumptions about the 'homogenisation' of mtpe texts, and at the same time encourages additional exploration of the data. for future work, we aim to conduct more extensive analyses on this data, e.g., by exploring if translation workflow influences different metrics of syntactic equivalence @xcite . as winters and kenny suggest, studies like this "usually branch into richer qualitative analyses on the basis of their initial quantitative findings" @xcite . this is precisely what we aim to do. we are currently annotating all texts on the basis of units of creative potential and creative shifts @xcite , multi-word units @xcite , and translation relations @xcite in order to get a more in-depth understanding of translation choices and how they are (not) mediated by the different workflows.