KATTA TIL MODELLARI YORDAMIDA BADIIY TARJIMADA SHEVA BELGILARINI QAYTA YARATISH: NEYTRALLASHTIRISH VA KOMPENSATSIYA MUAMMOLARI
DOI:
https://doi.org/10.67227/c837n045Kalit so‘zlar:
katta til modellari, badiiy tarjima, sheva, neytrallashtirish, kompensatsiya, normalizatsiya, personaj ovozi, tarjima strategiyasi.Abstrak
Maqolada katta til modellari yordamida badiiy tarjimada sheva belgilarini qayta yaratish muammosi nazariy jihatdan tahlil qilinadi. Texnik normalizatsiya va badiiy neytrallashtirish o‘rtasidagi farqlar aniqlanib, kompensatsiya strategiyalarini qo‘llash shartlari belgilanadi. Natijada tarjima qarorlari, funksional yo‘qotishlar va qoldiq cheklovlarni hujjatlashtirish tartibi taklif etiladi. Mazkur yondashuv empirik sinovdan o‘tkazilmagan.
Yuklashlar
Havolalar
1. Szymańska I. The Treatment of Geographical Dialect in Literary Translation from the Perspective of Relevance Theory // Research in Language. – 2017. – Vol. 15, No. 1. – P. 61–77. – DOI: 10.1515/rela-2017-0004
2. Messner C., Lippincott T. Examining Language Modeling Assumptions Using an Annotated Literary Dialect Corpus // Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities. – Miami: Association for Computational Linguistics, 2024. – P. 325–330. – DOI: 10.18653/v1/2024.nlp4dh-1.32
3. Alam M. M. I., Anastasopoulos A. Large Language Models as a Normalizer for Transliteration and Dialectal Translation // Proceedings of the Twelfth Workshop on NLP for Similar Languages, Varieties and Dialects. – Association for Computational Linguistics, 2025. – P. 39–67. – Source
4. Karpinska M., Iyyer M. Large Language Models Effectively Leverage Document-Level Context for Literary Translation, but Critical Errors Persist // Proceedings of the Eighth Conference on Machine Translation. – Singapore: Association for Computational Linguistics, 2023. – P. 419–451. – DOI: 10.18653/v1/2023.wmt-1.41
5. Sun J., Sellam T., Clark E., Vu T., Dozat T., Garrette D., Siddhant A., Eisenstein J., Gehrmann S. Dialect-Robust Evaluation of Generated Text // Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Vol. 1: Long Papers. – Toronto: Association for Computational Linguistics, 2023. – P. 6010–6028. – DOI: 10.18653/v1/2023.acl-long.331
6. Zhang R., Zhao W., Eger S. How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs // Proceedings of NAACL 2025: Human Language Technologies. Vol. 1: Long Papers. – Albuquerque: Association for Computational Linguistics, 2025. – P. 10961–10988. – DOI: 10.18653/v1/2025.naacl-long.548
