Zelin Xiao

doi:10.70711/aitr.v3i6.8599

AI as "Co-translator": The Creative Potential and Limitations of HumanMachine Collaboration in Literary Translation

Zelin Xiao

Abstract

This paper examines the evolving role of Artificial Intelligence (AI) as a collaborative agent, or "co-translator, " within the specialized domain of literary translation. Moving beyond the binary of tool versus rival, the analysis frames AI integration through a collaborative
lens, drawing on established theories from Translation Studies and contemporary Human-Computer Interaction (HCI). A systematic literature
review synthesizes existing scholarship on machine translation (MT) in literary contexts, identifying key gaps concerning creative process,
stylistic negotiation, and ethical authorship. Through a critical analysis of AI's capabilitiesincluding pattern recognition, semantic mapping,
and generative suggestionagainst its limitations in contextual understanding, cultural embeddedness, and aesthetic intentionality, the paper
argues for a synergistic model of human-machine collaboration. This model repositions the human translator as a creative director, strategic
editor, and cultural mediator who harnesses AI's computational power for ideation and efficiency while retaining sovereign authority over
literary voice and nuance. The paper concludes that AI's most profound impact may be less on the final product and more on the translation
process itself, prompting a re-evaluation of translational creativity and paving the way for new, hybrid forms of literary practice.

Keywords

AI-assisted translation; Literary translation; Human-machine collaboration; Co-translator; Neural machine translation; Translation process

Full Text:

PDF

Included Database

References

[1] Bowker, L. (2002). Computer-Aided Translation Technology: A Practical Introduction. University of Ottawa Press.

[2] Braidotti, R. (2013). The Posthuman. Polity Press.

[3] Doherty, S., & Kenny, D. (2021). Human versus machine translation: The cognitive load of post-editing. In The Routledge Handbook of

Translation and Technology (pp. 379-395). Routledge.

[4] Genzel, D., Uszkoreit, J., & Och, F. (2010). "Poetic" Statistical Machine Translation: Rhyme and Meter. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing.

[5] Koehn, P. (2020). Neural Machine Translation. Cambridge University Press.

[6] Koponen, M., et al. (2021). Post-editing and the cognitive load of literary translation. Translation, Cognition & Behavior, 4(1), 92-113.

[7] Resende, N., & Way, A. (2023). Creative Friction: Human Translators and AI as Collaborative Partners. Journal of Specialised Translation, (39), 45-67.

[8] Toral, A., & Way, A. (2018). What level of quality can Neural Machine Translation attain on literary text? Translation Quality Assessment, 1, 263-287.

[9] Venuti, L. (2017). The Translator's Invisibility: A History of Translation (2nd ed.). Routledge.

DOI: http://dx.doi.org/10.70711/aitr.v3i6.8599

Refbacks

There are currently no refbacks.