Reader reception of computational literary translations: the question of creativity
Kyo Gerrits, Ana Guerberof Arenas
University of Groningen
In this pilot, we explore how readers respond to literary translation across different translation modalities—machine translation (MT), post-editing (PE) and human translation (HT)—with a particular focus on creativity in the translations. Creativity is a crucial feature of literary texts, and equally so for translations of literary texts (Albaladejo et al., 2018). Whereas informational translation is mainly focused on bringing the message across as clearly as possible, literary translation is also invested in rendering the style of a text properly. For literature, language is not merely a vehicle for conveying a message, but an intrinsic part of its being.
This dual focus focus—on both the meaning and on the aesthetic form and presentation—has to be incorporated in a translation as well, necessitating creative solutions to convey the doubled use of language well. This focus on both the meaningful and aesthetic nature of a text at the same time can be a problem for NMT. Studies have shown that MT diminishes authorial voice (Kenny & Winters, 2020) and the more creative a text is, the less useful MT is considered to be (Moorkens et al., 2018). Luckily, there is more and more research into the use and performance of MT on literary translation (Toral et al., 2023; Matusov, 2019; Kuzman et al., 2019). However, there is still little research concerning the readers of these translations: what do readers think about literary MT? How does it affect their reading experiences, also compared to human translations? How are post-edited translations received? And how do the readers respond to the creative shifts of the different translation modalities?
This study replicates a study by Guerberof Arenas & Toral (2023), which analysed overall reader experience across the different translation modalities, using a survey on engagement, enjoyment, and reception, of a literary English-to-Dutch text. Our present study aims to delve deeper into the results of the previous study, by incorporating eye tracking for detailed on-line quantitative measures and a retrospective think-aloud protocol for detailed qualitative responses to the translation modalities. We focus specifically on the more creative segments of the text, analysing how readers engage with these segments individually and in the context of the entire text. We investigate whether these creative segments influence overall reading experiences and preferences for specific translation modalities.
This pilot study probes deeper into the findings of the previous study, adopting a mixed-method approach, integrating eye-tracking data and retrospective think-aloud interviews with the survey data from the previous study. This triangulation aims to reveal insights about engagement with creative shifts, their noticeability, and their impact on reading experiences across different translation modalities. By combining quantitative eye-tracking data with qualitative interviews, we aim to provide a comprehensive understanding of how literary MT is perceived by readers and its implications for the use of NMT for literary translation.
References
Albaladejo, T., & Chico-Rico, F. (2018). Translation, Style, and Poetics. In S. Harding & O.C. Cortés (Eds.), The Routledge Handbook of Translation and Culture (pp. 115-133). Routledge.
Guerberof Arenas, A., & Toral A. (2023). To Be or Not to Be: A Translation Reception Study of a Literary Text Translated into Dutch and Catalan Using Machine Translation. ArXiv.
Kenny, D., & Winters, M. (2020). Machine Translation, Ethics and the Literary Translator’s Voice. Translation Spaces, 9(1), pp. 123-149.
Kuzman, T., Vintar, S., & Arčan, M. (2019). Neural Machine Translation of Literary Texts from English to Slovene. Proceedings of the Qualities of Literary Machine Translation, pp. 1-9.
Matusov, E. (2019). The Challenges of Using Neural Machine Translation for Literature. Proceedings of the Qualities of Literary Machine Translation, pp. 10-19.
Moorkens, J., Toral, A., Castilho, S., & Way, A. (2018). Translators’ Perception of Literary Post-Editing Using Statistical and Neural Machina Translations. Translation Spaces, 7(2), pp. 240-262.
Toral, A., Van Cranenburgh, A., & Nutters, T. (2023) Literary-Adapted Machine Translation in a Well-Resourced Language Pair: Explorations with More Data and Wider Contexts. In A. Rothwell, A. Way, & R. Youdale (Eds.), Computer-Assisted Literary Translation (pp. 27-52). Routledge.