A collaborative research experiment supported by the John Fell OUP Research Fund 2024-25, an AI Competency Centre Fast Exploration Grant 2024-26, and an Impact Leaders Award 2025-26. 

Participants
Matthew Reynolds (PI), Joseph Hankinon (PDRA)
Mary Newman, Sarah Ekdawi (OCCT/Oxford); Wen-Chin Ouyang,Yingxin Chen, Rafaa Ragebi, Xuemeng Zhang (SOAS); Vani Nautiyal (IIT Roorkee); Deepshikha Behera (Hyderabad); Annmarie Drury (CUNY); Karen Cresci, Luciana Beroiz, María Eugenia Rigane (Mar del Plata); Ellie Pavlick, Lachlan Kermode (Brown)
(Participants October 2024 - March 2025: Georgina Fooks, Rowan Anderson, Alyssa Ollivier-Tabakushvili, Karima Brooke, Catriona Perry, Kathy Fan, Tinashe Mushakavanhu (OCCT/Oxford); Kathryn Eccles, Ana Valdivia, Yui Kondo (Oxford Internet Institute); Ret'sepile Makamane, Cosima Bruno, (SOAS); Kọ́lá Túbọ̀sún (OlongoAfrica.com); Lisa Rose Bradford, Vanesa Venditti, Victoria Gisele Chacón Oribe (Mar del Plata); Chakidipta Baruah, Sajal Pathak, Aanya Mehta (Delhi); Xinyi Wang (Tsinghua); Sylee Gore, Maggie Wang).      

The field of translation studies faces two major challenges. The first is decolonial critique. Scholars such as Makoni, Makalela and Sakai have dismantled the picture of languages as separate, standardised entities with translation operating between them and have shown it to be a colonial construction. Instead, they present language difference as a variable continuum, with translation as one of the means for negotiating it. Translation can follow the colonial blueprint, supporting the standardisation of languages and a restricted conception of equivalent meaning (this is in fact how it is used in many professional contexts). But it can also work to decolonial effect, activating the multilingual and varied language competences that most people possess. I have called this mode of translation 'translationality' (2016): it foregrounds process and collaboration, making visible the complexity of language differences and discovering new ways of traversing them.

The second challenge is the rapid improvement of machine translation and, now, the advent of generative AI. This has created uncertainty, with some voices asserting that human translators can be replaced by machines, while others insist that human involvement will always be required, especially for literary translation. Strikingly, the debate is happening almost entirely within the picture of translation that has been shown to be a colonial construction - i.e., a matter of success or failure in achieving equivalence of meaning between standard languages conceived as separate.

This picture fails to capture what is new about generative AI tools relation to translation: the fact that they can handle language variety in more particular ways than longer-established tools such as Google Translate. Those older tools work within the colonial regime of standardised languages, translating between something defined as eg 'Italian' and something defined as eg 'Swahili'. But LLMs such as Chat-GPT, Gemini and Claude, though still involved in standardisation, present a more nuanced landscape of language variation. You can ask them to translate into a given style, or professional register, or dialect, or the language of a historical period, or a mix of languages. You can prompt them to use a particular rhyme-scheme, or preserve a sound pattern, or use only words of one syllable. The responses to these questions are of varying fruitfulness (variable between tools, between languages involved, and between questions); but they are interesting. They show that machine translation is not necessarily the enemy of linguistic diversity and play: it has the potential to contribute to creative, decolonial translation practices.

Our collaborative experiments are investigating this potential. Focusing on poetry (typically excluded from public discourse around machine translation), we are trying out how LLMs can join in creative translation practices involving a range of languages, both low-resource and high-resource, and exploring related political, technical and conceptual issues. The ongoing experiment has been punctuated by symposia at which we have heard from and discussed with other researchers and practitioners.

Sometimes - especially with low-resource languages - what has been discovered is a barrier, misrepresentation or lack: in these cases our aim has been to articulate what would be needed for creative-translational collaboration with AI to work better. Dialogues with the Language Understanding and Representation (LUNAR) Lab at Brown University and the Oxford Internet Institute has enabled us to do this in an informed way, as well as to clarify what is revealed by happier discoveries. The thrust of the project is to be interested in the possibilities of LLMs for creative, decolonial translation, and for what they can show us about language and creativity, rather than to repeat familiar narratives of their limitations. Mindful of the commercial aspect of LLMs, their environmental cost, and their repercussions for the translation profession, we have been giving attention also to open-source and smaller models; we seek to show how our experiments can increase awareness of the importance of language diversity online and stimulate participation in creative translation practices. We hope to expand the understanding of translation in AI, and AI in translation, to include both creativity - conceived here as collaborative and distributed, more Oulipo than Romantic - and a decolonial engagement with language variety and resource inequality.

The open access book of the project, AI, Decoloniality and Creative Poetry Translation: The Promise of Translalia eds Matthew Reynolds and Joseph Hankinson, will be published by UCL Press in 2027. 

We have developed an app, Translalia, to support ethically aware translation practices with AI, putting human choice at the heart of the process. It can be sampled here.