Zamonaviy mashina tarjimasining asosiy tushunchalari va terminologiyasi

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Imami, T. R., Mu’in, F., & Nasrullah, N. (2021). Linguistic and Cultural Problems in Translation. In Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research. https://doi.org/10.2991/assehr.k.211021.024

Khurana, D., Koli, A., Khatter, K., & Singh, S. (2022). Natural language processing: state of the art, current trends and challenges. In Multimedia Tools and Applications (Vol. 82, Issue 3, p. 3713). Springer Science+Business Media. https://doi.org/10.1007/s11042-022-13428-4

Okur, B. C., TAKCI, H., & Akgül, Y. S. (2013). Rewriting Turkish texts written in English alphabet using Turkish alphabet (p. 1). https://doi.org/10.1109/siu.2013.6531394

Papineni, K. (2002). Machine Translation Evaluation: N-grams to the Rescue. In Language Resources and Evaluation. Springer Science+Business Media. http://www.lrec-conf.org/proceedings/lrec2002/pdf/347.pdf

Seraji, M. (2015). Morphosyntactic Corpora and Tools for Persian. http://www.diva-portal.org/smash/record.jsf?pid=diva2:800998

Timalsina, R. (2023). Overcoming Intercultural Obstacles in Translation. In Dristikon A Multidisciplinary Journal (Vol. 13, Issue 1, p. 156). https://doi.org/10.3126/dristikon.v13i1.56096

Koehn, P. (2010). Statistical Machine Translation. Cambridge University Press.

Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems.

Zakharov, Victor & Tao, Yuan. (2015). Разработка и использование параллельного корпуса русского и китайского языков. НТИ. Сер. 2. ИНФОРМ. ПРОЦЕССЫ И СИСТЕМЫ.

Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The State and Fate of Linguistic Diversity and Inclusion in the NLP World. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2004.09095

Kakum, N., Laskar, S. R., Sambyo, K., & Pakray, P. (2023). Neural machine translation for limited resources English-Nyishi pair. In Sadhana (Vol. 48, Issue 4). Springer Science+Business Media. https://doi.org/10.1007/s12046-023-02308-8

Nekoto, W., Marivate, V., Matsila, T., Fasubaa, T., Fagbohungbe, T., Akinola, S. O., Muhammad, S. H., Kabenamualu, S., Osei, S., Sackey, F., Niyongabo, R. A., Macharm, R., Ogayo, P., Ahia, O., Berhe, M. M., Adeyemi, M., Mokgesi-Selinga, M., Okegbemi, L., Martinus, L., … Bashir, A. (2020). Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages. https://doi.org/10.18653/v1/2020.findings-emnlp.195

Nigatu, H. H., Tonja, A. L., Rosman, B., Solorio, T., & Choudhury, M. (2024). The Zeno’s Paradox of `Low-Resource’ Languages. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2410.20817

Ranathunga, S., & Silva, N. de. (2022). Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2210.08523

Khurana, D., Koli, A., Khatter, K., & Singh, S. (2022). Natural language processing: state of the art, current trends and challenges. In Multimedia Tools and Applications (Vol. 82, Issue 3, p. 3713). Springer Science+Business Media. https://doi.org/10.1007/s11042-022-13428-4

Rajput, A. E. (2019). Natural Language Processing, Sentiment Analysis and Clinical Analytics. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.1902.00679