SavedAdd your journal

Journals / transLogos Translation Studies Journal / 2026 / Cilt: 9 Sayı: 1

Stylistic Flattening and Terminological Displacement in AI-Mediated Legal Translation: Evidence from German–Greek and English–Greek Language Pairs

Stavroula Paraskevi Vraila(Ionian University)0009-0000-5404-3952
Pages
23–46
Publication Date
2026-06-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The article concludes by discussing implications for translator training, post-editing literacy, and the critical evaluation of AI-generated outputs in specialized contexts.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Large language models (LLMs) are increasingly being integrated into translation workflows.
📊
Key Findings & Data:Drawing on a purpose-built corpus of controlled outputs from two neural machine translation engines (DeepL and Google Translate) and two generative LLMs (OpenAI GPT-5.3 and Anthropic Claude Opus 4.5), tested on German–Greek and English–Greek legal terms, the study investigates how these systems, whose training data are heavily weighted towards Anglophone legal corpora, reduce register variation and impose Anglo-American categories onto continental legal traditions, particularly for concepts lacking direct equivalence across legal systems.
💡
Conclusion & Impact:The article concludes by discussing implications for translator training, post-editing literacy, and the critical evaluation of AI-generated outputs in specialized contexts.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:GenAI · specialized translation · legal translation

Abstract

Large language models (LLMs) are increasingly being integrated into translation workflows. However, their systemic impact on the quality of legal translation in non-hegemonic language pairs remains insufficiently explored. This article examines two interrelated phenomena, namely stylistic flattening and terminological displacement via English mediation, as observed in AI-mediated legal translation into Greek. Drawing on a purpose-built corpus of controlled outputs from two neural machine translation engines (DeepL and Google Translate) and two generative LLMs (OpenAI GPT-5.3 and Anthropic Claude Opus 4.5), tested on German–Greek and English–Greek legal terms, the study investigates how these systems, whose training data are heavily weighted towards Anglophone legal corpora, reduce register variation and impose Anglo-American categories onto continental legal traditions, particularly for concepts lacking direct equivalence across legal systems. The analysis reveals that stylistic leveling operates at the level of register and broader discourse conventions, while terminological displacement functions at the conceptual level, substituting source-system categories with common law analogues. Together, these mechanisms constitute what the article terms algorithmic legal hegemony, a structurally embedded asymmetry through which Anglophone legal reasoning colonizes target systems via ostensibly neutral technological mediation. The article concludes by discussing implications for translator training, post-editing literacy, and the critical evaluation of AI-generated outputs in specialized contexts.

🔖

Cite This Article

Vraila, S. P. (2026). Stylistic Flattening and Terminological Displacement in AI-Mediated Legal Translation: Evidence from German–Greek and English–Greek Language Pairs. transLogos Translation Studies Journal, Cilt: 9 Sayı: 1, 23–46. https://doi.org/10.29228/transLogos.86

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (27)

  1. Alcaraz Varó, Enrique. 2009. “Isomorphism and Anisomorphism in the Translation of Legal Texts.” In Translation Issues in Language and Law, edited by Frances Olsen, Alexander Lorz, and Dieter Stein, 189–192. London: Palgrave Macmillan.
  2. Bai, Yuntao, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, et al. 2022. “Constitutional AI: Harmlessness from AI Feedback.” arXiv:2212.08073v1. doi:10.48550/arXiv.2212.08073.
  3. Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” In FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. doi:10.1145/3442188.3445922.
  4. Biel, Łucja. 2014. Lost in the Eurofog: The Textual Fit of Translated Law. Frankfurt am Main: Peter Lang.
  5. Bommasani, Rishi, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, et al. 2021. “On the Opportunities and Risks of Foundation Models.” arXiv:2108.07258v1. doi:10.48550/arXiv.2108.07258.
  6. Briva-Iglesias, Vicent, João Lucas Cavalheiro Camargo, and Gokhan Dogru. 2024. “Large Language Models ‘Ad Referendum’: How Good Are They at Machine Translation in the Legal Domain?” MonTI: Monographs in Translation and Interpreting, no. 16, 75–107. doi:10.6035/MonTI.2024.16.02.
  7. Chu, Chenhui, and Rui Wang. 2018. “A Survey of Domain Adaptation for Neural Machine Translation.” In Proceedings of the 27th International Conference on Computational Linguistics, 1304–1319. Santa Fe, New Mexico: Association for Computational Linguistics. https://aclanthology.org/C18-1111.pdf.
  8. Cui, Ying, Xiao Liu, and Yuqin Cheng. 2023. “A Comparative Study on the Effort of Human Translation and Post-Editing in Relation to Text Types: An Eye-Tracking and Key-Logging Experiment.” SAGE Open 13 (1). doi:10.1177/21582440231155849.
  9. Dodge, Jesse, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner. 2021. “Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.” In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 1286–1305. Association for Computational Linguistics. doi:10.18653/v1/2021.emnlp-main.98.
  10. Fairclough, Norman. 1989. Language and Power. London: Longman.
  11. Fairclough, Norman. 2003. Analysing Discourse: Textual Analysis for Social Research. London: Routledge.
  12. Killman, Jeffrey. 2023. “Machine Translation and Legal Terminology.” In Handbook of Terminology, edited by Łucja Biel and Hendrik J. Kockaert, 3:485–510. Amsterdam: John Benjamins.
  13. Koehn, Philipp, and Rebecca Knowles. 2017. “Six Challenges for Neural Machine Translation.” In Proceedings of the First Workshop on Neural Machine Translation, 28–39. Vancouver: Association for Computational Linguistics. doi:10.18653/v1/W17-3204.
  14. Lakew, Surafel M., Mauro Cettolo, and Marcello Federico. 2018. “A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation.” In Proceedings of the 27th International Conference on Computational Linguistics, 641–652, Santa Fe, New Mexico, USA. Association for Computational Linguistics. https://aclanthology.org/C18-1054.pdf.
  15. Mattila, Heikki E. S. 2013. Comparative Legal Linguistics: Language of Law, Latin and Modern Lingua Francas. 2nd ed. Translated by Christopher Goddard. Farnham: Ashgate.
  16. Mossop, Brian. 2014. Revising and Editing for Translators. 3rd ed. London: Routledge.
  17. Ouyang, Long, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, et al. 2022. “Training Language Models to Follow Instructions with Human Feedback.” In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 27730–27744. doi:10.52202/068431-2011.
  18. Phillipson, Robert. 1992. Linguistic Imperialism. Oxford: Oxford University Press.
  19. Prieto Ramos, Fernando. 2021. “Translating Legal Terminology and Phraseology: Between Inter-systemic Incongruity and Multilingual Harmonization.” In “Legal Terminology and Phraseology in Translation,” edited by Fernando Prieto Ramos. Special Issue, Perspectives 29 (2): 175–183. doi:10.1080/0907676X.2021.1849940.
  20. Prieto Ramos, Fernando. 2024. “Revisiting Translator Competence in the Age of Artificial Intelligence: The Case of Legal and Institutional Translation.” In “Competence in Legal and Institutional Translation: Training Challenges and Innovations,” edited by Fernando Prieto Ramos. Special Issue, The Interpreter and Translator Trainer 18 (2): 148–173. doi:10.1080/1750399X.2024.2344942.
  21. Rossi, Caroline, and Jean-Pierre Chevrot. 2019. “Uses and Perceptions of Machine Translation at the European Commission.” The Journal of Specialised Translation, no. 31, 177–200. doi:10.26034/cm.jostrans.2019.182.
  22. Šarčević, Susan. 1997. New Approach to Legal Translation. The Hague: Kluwer Law International.
  23. Singhal, Prasann, Tanya Goyal, Jiacheng Xu, and Greg Durrett. 2023. “A Long Way to Go: Investigating Length Correlations in RLHF.” arXiv:2310.03716v1. doi:10.48550/arXiv.2310.03716.
  24. Utiyama, Masao, and Hitoshi Isahara. 2007. “A Comparison of Pivot Methods for Phrase-Based Statistical Machine Translation.” In Proceedings of NAACL-HLT 2007, 484–491. https://aclanthology.org/N07-1061/.
  25. Vieira, Lucas Nunes. 2024. “Machine Translation and Migration.” In The Routledge Handbook of Translation and Migration, edited by Brigid Maher, Loredana Polezzi, and Rita Wilson, 221–234. London: Routledge.
  26. Vieira, Lucas Nunes, Minako O’Hagan, and Carol O’Sullivan. 2021. “Understanding the Societal Impacts of Machine Translation: A Critical Review of the Literature on Medical and Legal Use Cases.” Information, Communication & Society 24 (11): 1515–1532. doi:10.1080/1369118X.2020.1776370.
  27. Zacharopoulos, Vassilios, Christina Koutsogianni Hanke, Dionysia Bourgiezi, Antonia Paradelli, Christina Papatsori, Evdokia Priovolou, and Vassilis Triantafyllidis. 2013. Deutsch–Griechisches / Griechisch-Deutsches Rechtswörterbuch [German–Greek / Greek–German legal dictionary]. Edited by Chrisoula Tsepisi and Konstantinos Vathiotis. Athens: Nomiki Bibliothiki.