Final Call for Papers - StyGenAI Workshop @EAMT 2026
The StyGenAI workshop will take place in conjunction with EAMT 2026 (European Association for Machine Translation) from 15–18 June 2026 at the Schaumburg Concertzaal in Tilburg, the Netherlands. The workshop will be part of a vibrant international conference bringing together researchers and practitioners at the forefront of machine translation and language technologies. More information about the main conference is available at https://eamt2026.org/ → Important Dates
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Workshop paper submission deadline: 27 April 2026 (there will be no extension to this deadline) -
Notification of acceptance: 12 May 2026 -
Camera-ready papers due: 20 May 2026
→ Submission Information
The workshop invites research papers reporting original work. Papers should be 4–10 pages in length, excluding references. Accepted papers will be published in the workshop proceedings and made available online via the ACL Anthology. Submissions must follow the EAMT 2026 formatting guidelines and templates https://eamt2026.org/calls-for-papers and be submitted via the EasyChair https://easychair.org/conferences/submission_new?a=35628372 system. Workshop website: https://sites.google.com/view/workshopstygenai?usp=sharing
About the workshop:
The advent of Generative Artificial Intelligence has profoundly reshaped the landscape of translation. Moving beyond traditional machine translation paradigms, large language models (LLMs) now operate as translation agents capable of producing linguistically fluent and stylistically complex texts. As a result, translation is no longer only a matter of accuracy or adequacy, but increasingly one of style.
As LLMs are adopted for translation tasks, their outputs reveal distinctive linguistic and stylistic patterns. These patterns differ in subtle but consequential ways from those found in both human translation and conventional MT systems. While such differences are often perceived intuitively by readers and practitioners, they remain underexplored from a systematic, research-driven perspective.
This evolving scenario raises a set of pressing questions:
-What are the stylistic features of GenAI-produced translations?
-How do they differ from those generated by traditional MT systems?
-And how do they compare to human translations across genres, languages, and contexts?
StyGenAI is the first workshop dedicated specifically to the study of style in GenAI-translated content. The workshop aims to bring together researchers interested in AI translation stylistics, including recurrent stylistic patterns, departures from human translation style, and the linguistic, technical, and contextual factors that modulate AI-generated output. Particular attention is given to the role of text genre, language pair, and prompt design or prompting strategies in shaping the stylistic profile of GenAI translations.
The workshop provides a forum for interdisciplinary dialogue at the intersection of translation studies, computational linguistics, stylistics, and AI evaluation. Contributions are welcomed from both empirical and conceptual perspectives, as well as from research that bridges academic inquiry and professional practice.
The workshop welcomes empirical, methodological, and conceptual contributions from translation studies, computational linguistics, stylistics, and related fields.
We invite submissions addressing, but not limited to, the following topics:
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Stylistic fingerprints of LLM-based translation in comparison with conventional MT systems and professional human translation -
The influence of prompting strategies (for example, role prompting, constraints, zero-shot, one-shot, and few-shot approaches) on stylistic outcomes -
Cross-genre analyses of AI translation style in literary, academic, journalistic, technical, and social media texts -
Authorial voice and style preservation across languages in GenAI-mediated translation -
Language-specific manifestations of machine-like or synthetic stylistic patterns -
Methodologies for evaluating stylistic adequacy and stylistic variation in AI-generated translations -
Cognitive effort and decision-making in post-editing LLM output for stylistic quality as opposed to content accuracy -
Cultural, pragmatic, or discourse-level mismatches introduced by AI translation choices -
The handling of irony, humour, voice, and other stylistically marked devices in GenAI translation -
Human–AI hybrid workflows for style-sensitive translation tasks -
Pedagogical approaches to training translators to identify, assess, and correct AI-generated stylistic patterns -
Diachronic or longitudinal analyses of stylistic change as LLMs and prompting practices evolve