Simultaneous Interpreting and AI Text-Mediated Output A Comparative Analysis of English–Greek Institutional Discourse


Published: Feb 20, 2026
Keywords:
artificial intelligence simultaneous interpreting speech translation communicative adequacy
Anastasios Ioannidis
https://orcid.org/0000-0003-3990-1140
Abstract

This study investigates how closely AI-based text-mediated output approximates professional human simultaneous interpreting with respect to target-language output characteristics in a high-stakes institutional context. Using three plenary speeches from the European Parliament, it compares official Greek simultaneous interpretation with AI-generated Greek output produced via a neural machine translation system. The analysis combines proposition-level error annotation with qualitative discourse-oriented analysis. While the AI output displays fewer overall errors, it shows systematic limitations in pragmatic appropriateness, evaluative intensity, terminology, and stylistic naturalness. Human interpreting, by contrast, demonstrates greater sensitivity to contextual cues and audience-oriented reformulation, despite strategic information compression.

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References
Bernardini, S., Ferraresi, A., Russo, M., Collard, C. & Defrancq, B. (2018). Building interpreting and intermodal corpora: a how-to for a formidable task. In M. Russo, C. Bendazzoli & B. Defrancq (eds) Making way in corpus-based interpreting studies (pp. 21-42). Singapore: Springer.
Cao, Y. (2024). Comparative analysis of machine interpreting and human interpreting: Insights into consecutive interpreting teaching. In M. F. b. Sedon, I. A. Khan, M. C. Birkök & K. S. Chan (eds), Proceedings of the 2024 International Conference on Social Sciences and Humanities (pp. 730-741). Paris: Atlantis Press.
Cela Gutiérrez, C. (2025). AI-assisted Interpreting: Valuable Tool for Professional Interpreters or Job Displacement? Traduction et Langues, 24 (1), 263-281.
Fantinuoli C. (2022). Conference interpreting and new technologies. In Michaela Albl-Mikasa and Elisabet Tiselius (eds), Routledge Handbook of Conference Interpreting (pp. 508-522). New York: Routledge.
Fantinuoli, C. (2019). The technological turn in interpreting: The challenges that lie ahead. In W. Baur & F. Mayer (eds) Proceedings of the Conference Übersetzen und Dolmetschen 4.0 – Neue Wege im digitalen Zeitalter. (pp. 334-354). Berlin: BDÜ Fachverlag.
Fantinuoli, C. and Prandi, B. (2021). Towards the evaluation of automatic simultaneous speech translation from a communicative perspective. In M. Federico, A. Waibel, M. R. Costa-jussà, J. Niehues, S. Stuker, & E. Salesky (eds), Proceedings of the 18th International Conference on Spoken Language Translation (pp. 245-254). Stroudsburg: Association for Computational Linguistics.
Horváth, I. (2021). Speech translation vs. Interpreting. Language Studies and Modern Humanities, 3(2), 174–187. Berlin: Russian State Pedagogical University Herzen.
Lee, J.-H. & Cha, K.-W. (2023). Human interpretation and machine translations based upon interviews with director Joon-ho Bong. Korean Journal of English Language and Linguistics, 23, 204-219.
Liu, Y. & Liang, J. (2024). Multidimensional comparison of Chinese–English interpreting outputs from human and machine: Implications for interpreting education in the machine-translation age. Linguistics and Education, 80, 101273, 1-13.
Lu, X.-L. & Han, C. (2023). Automatic assessment of spoken-language interpreting based on machine-translation evaluation metrics: A multi-scenario exploratory study. Interpreting: International Journal of Research and Practice in Interpreting, 25(1), 109-143. Amsterdam/Philadelphia: John Benjamins Publishing Company.
Nimdzi (2019). The Nimdzi Interpreting Index. [Online]. Available at: https://www.nimdzi.com/the-2019-nimdzi-interpreting-index/ (accessed 02.01.2026).
Peeters, K., Daems, J., Plieseis, C., Rivas Ginel, M. I., Şahin, M. (2025). AI for Translation and Interpreting. A Roadmap for Users and Policy Makers. Conseil Européen pour les Langues / European Language Council.
Russo, M. (2019). Corpus-based studies in conference interpreting. Slovo.ru: Baltijskij accent, 10(1), 87-100.
Sperber, M., de Seyssel, M., Bao, J., & Paulik, M. (2025). Toward Machine Interpreting: Lessons from Human Interpreting Studies. In C. Christodoulopoulos, T. Chakraborty, C. Rose & V. Peng (eds), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 23349–23364). Suzhou, China: Association for Computational Linguistics.
Tan, Y. L. & Gao, L. (2025). Pointing to context from a Relevance Theory perspective: A comparative study of human and machine interpreting. Mediterranean Journal of Social Sciences, 16(3), 1-14.
Tiselius, E. (2009). Revisiting Carroll’s scales. In C. V. Angelelli & H. E. Jacobson (eds), Testing and Assessment in Translation and Interpreting Studies (pp. 95-121). Amsterdam/Philadelphia: John Benjamins Publishing Company.
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