]THE IMPACT OF MACHINE TRANSLATION ON THE TRANSFER OF PRAGMATIC MEANING: AN EMPIRICAL ENGLISH–UZBEK PERSPECTIVE
Keywords:
machine translation; neural machine translation; pragmatic meaning; English–Uzbek translation; AI translation; intercultural pragmatics; speech acts; politeness strategies; computational linguistics; pragmatic equivalenceAbstract
The rapid expansion of artificial intelligence technologies and neural machine translation systems has significantly transformed global intercultural communication and multilingual information exchange (Koehn, 2020; O’Brien, 2023). Contemporary machine translation tools such as Google Translate, DeepL, Microsoft Translator, and ChatGPT-based systems increasingly demonstrate high levels of grammatical fluency and semantic accuracy. Nevertheless, despite substantial technological progress, machine translation continues to encounter serious difficulties in transferring pragmatic meaning, particularly between linguistically and culturally distant languages such as English and Uzbek (House, 2015; Kecskes, 2014).
The present study investigates the effectiveness of AI-based machine translation in preserving pragmatic equivalence in English–Uzbek translation. The research employs a mixed-method corpus-based design combining qualitative pragmatic analysis and quantitative statistical evaluation. A corpus consisting of 160 English expressions containing pragmatic elements was analyzed. The dataset included idiomatic expressions, indirect speech acts, politeness strategies, conversational implicatures, sarcastic statements, humor, and culturally marked units. The selected materials were translated into Uzbek using Google Translate, DeepL, ChatGPT, and Microsoft Translator. Machine-generated translations were compared with expert human translations based on pragmatic equivalence, contextual appropriateness, communicative naturalness, and sociocultural adaptation (Baker, 2018).
The findings demonstrate that while AI systems successfully transfer denotative semantic meaning in informational texts, they frequently fail to preserve implicit communicative intention, politeness hierarchy, cultural nuance, figurative meaning, and discourse-sensitive pragmatics. Quantitative analysis revealed that idiomatic expressions and sarcastic statements produced the highest rates of pragmatic failure, while indirect speech acts demonstrated relatively higher translation accuracy. Among the analyzed platforms, ChatGPT-based translation showed comparatively stronger contextual adaptation, although substantial limitations remained in culturally embedded discourse.
The study argues that pragmatic competence remains one of the most challenging dimensions of machine translation because pragmatic meaning depends heavily on sociocultural cognition, contextual inferencing, and intercultural communicative norms (Levinson, 1983; Verschueren, 2012). The article contributes to translation studies, intercultural pragmatics, and computational linguistics by proposing an empirical framework for evaluating pragmatic equivalence in low-resource language pairs such as English and Uzbek.
References
Austin, J. L. (1962). How to Do Things with Words. Oxford University Press.
Baker, M. (2018). In Other Words: A Coursebook on Translation (3rd ed.). Routledge.
Brown, P., & Levinson, S. (1987). Politeness: Some Universals in Language Usage. Cambridge University Press.
House, J. (2015). Translation Quality Assessment: Past and Present. Routledge.
Hutchins, W. J., & Somers, H. L. (1992). An Introduction to Machine Translation. Academic Press.
Kecskes, I. (2014). Intercultural Pragmatics. Oxford University Press.
Koehn, P. (2020). Neural Machine Translation. Cambridge University Press.
Levinson, S. C. (1983). Pragmatics. Cambridge University Press.
Nida, E. (1993). Language, Culture, and Translating. Shanghai Foreign Language Education Press.
O’Brien, S. (2023). Machine translation and artificial intelligence: Current developments and future directions. Translation Spaces, 12(1), 15–34.
Searle, J. R. (1969). Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press.
Toral, A., & Way, A. (2021). Is neural machine translation ready for literary translation? Language Resources and Evaluation, 55(1), 47–67.
Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
Verschueren, J. (2012). Ideology in Language Use: Pragmatic Guidelines for Empirical Research. Cambridge University Press.
Yule, G. (1996). Pragmatics. Oxford University Press.
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