The DUAL-T project was particularly innovative in terms of (a) its inclusion of literary translators and a literary translation technology company in the research process, (b) the comparison of word processors, machine translation post-editing, and CAT tools for literary translation, and (c) a methodology combining user testing techniques, interviews, and focus groups.
Some of the main results of the project include the following:
1. Post-editing requires less cognitive, temporal, and technical effort compared to Word and CAT tools, but the pause ratio is higher when post-editing, potentially indicating changes in the type of cognitive activity involved.
2. There is a disconnect between measured and perceived effort, as literary translators tend to perceive Microsoft Word as the fastest workflow, despite it being the slowest in most cases.
3. Saving time and effort is not necessarily a priority for literary translators and is sometimes seen as counterproductive for literary translation.
4. Literary translators see post-editing as "dangerous" and interfering with their process. Many fear that post-editing could make them "lazy," leading them to accept or overlook errors in their translations.
5. Industry and literary translators conceive of the role of technology in literary translation differently: literary translators worry that increased automation could take away both their agency and the "fun" of the translation process, while technology producers see post-editing as a way to translate texts that would not have been translated otherwise, providing literary translators with a more stable income and a steady flow of work.
Overall, DUAL-T crossed disciplinary boundaries and centred literary translators’ behaviour and attitudes in the study of literary translation technology. The project opened new avenues for future work on co-creating workflows informed by literary translators’ professional narratives. Future research could investigate ways to further involve literary translators in research as a community of practice, experiment with longer texts, different tools (e.g. Generative AI), and a wider range of language pairs and literary genres. It could also develop new tools that account for literary translators’ needs and investigate the long-term impact of technology-inclusive workflows on literary translation quality and translators’ working conditions.