Page 512 - XORIJIY TILLARNI O‘QITISH VA TARJIMA SOHASIDA SUN’IY INTELLEKTDAN SAMARALI FOYDALANISHNING ZAMONAVIY TENDENSIYALARI

REFERENCES
1. Abiyatova, M. M. (2025). Skopos theory in translation. Samarkand State
Institute of Foreign Languages.
2. Al Salem, M. N., Alghazo, S., et al. (2026). How AI translation personas
reframe diplomatic texts. Studies in Media and Communication. (January
2026)
3. Berman, A. (1985). Translation and the trials of the foreign. In L. Venuti (Ed.
& Trans.), The translation studies reader (pp. 276–289). Routledge.
4. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., …
Amodei, D. (2020). Language models are few-shot learners. Advances in
Neural Information Processing Systems, 33, 1877–1901.
5. Court, S., & Elsner, M. (2024). Shortcomings of LLMs for low-resource
translation: Retrieval and understanding are both the problem. ACL
Anthology.
6. Feng, C. (2025). Analysis of semantic deviation in AI translation and
linguistic optimization paths. International Scientific Technical and
Economic Research. (June 2025)
7. Goddard, C. (2020). Overcoming the linguistic challenges for ethno-
epistemology. [Book chapter]. (May 2020)
8. Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The state
and fate of linguistic diversity and inclusion in the NLP world. Proceedings
of the 58th Annual Meeting of the Association for Computational
Linguistics, 6282–6293.
9. Jumashukurov, A. (2024). The quality of translation of Turkic languages by
AI translation tools. Journal of Student Research. (November 2024)
10. Martin, J. R., & White, P. R. R. (2005). The language of evaluation: Appraisal
in English. Palgrave Macmillan.

11. Nekoto, W., Marivate, V., Matsila, T., Fasubaa, T., Fagbohungbe, T., Akinola, S.
O., … Bashir, A. (2020). Participatory research for low-resourced machine
translation: A case study in African languages. Findings of EMNLP 2020,
2144–2160.
12. Pang, J., Ye, F., Wong, D. F., Yu, D., Shi, S., Tu, Z., & Wang, L. (2025). Salute the
classic: Revisiting challenges of machine translation in the age of large
language models. Transactions of the Association for Computational
Linguistics, 13, 73–95. https://doi.org/10.1162/tacl_a_00730
13. Park, J. (2022). A comparative study of AI translation: Google Translate and
Papago. Studies in Linguistics. (October 2022)
14. Pucinskaite, J., & Mitkov, R. (2025). Evaluating the LLM and NMT models in
translating low-resourced languages. ACL Anthology.
15. Ramzan, K., Sadiq, A., & Noor, A. (2025). Comparative analysis of Google
translation and human translation through the lens of Skopos theory.
Journal of Applied Linguistics and TESOL (JALT), 8(1), 1938–1949. 510

IV SHO‘BA:

Tarjimashunoslikda sun’iy intellektdan foydalanishning lingvistik
muammolari va funksional imkoniyatlari
https://www.asr-conference.com/