Integration and Innovation of Artificial Intelligence and Traditional English Translation Methods

[1] Fernandez-Gonzalez, D., & Gomez-Rodriguez, C. (2023). Discontinuous grammar as a foreign language. Neurocomputing. Search in Google Scholar

[2] Zhang, S. (2021). Automatic translation of medical information based on text feature recognition. International Journal on Artificial Intelligence Tools. Search in Google Scholar

[3] Marta R. Costa-jussà. (2018). From feature to paradigm: deep learning in machine translation. Journal of Artificial Intelligence Research. Search in Google Scholar

[4] Berdejo-Espinola, V., & Amano, T. (2023). Al tools can improve equity in science. Science. Search in Google Scholar

[5] Mirjam Sepesy Maučec, & Brest, J. (2017). Slavic languages in phrase-based statistical machine translation: a survey. Artificial Intelligence Review, 51(2), 1-41. Search in Google Scholar

[6] Anandika, A., Chakravarty, S., & Paikaray, B. K. (2023). Named entity recognition in odia language: a rule-based approach. International Journal of Reasoning-based Intelligent Systems, 15(1), 15-. Search in Google Scholar

[7] Ding, D. (2017). Application of fuzzy neural network model in the evaluation of translation quality of scientific and technical documents. Revista de la Facultad de Ingenieria, 32(2), 741-750. Search in Google Scholar

[8] Stahlberg, F. (2020). Neural machine translation: a review and survey. Journal of Artificial Intelligence Research. Search in Google Scholar

[9] Nguyen, T., Le, H., & Pham, V. H. (2020). Source-word decomposition for neural machine translation. Mathematical Problems in Engineering, 2020. Search in Google Scholar

[10] Li, P., Ning, Y., & Fang, H. (2021). Artificial intelligence translation under the influence of multimedia teaching to study english learning mode. International Journal of Electrical Engineering Education, 002072092098352. Search in Google Scholar

[11] Lihua, Z. (2022). The relationship between machine translation and human translation under the influence of artificial intelligence machine translation. Mobile Information Systems. Search in Google Scholar

[12] Karakanta, A., Dehdari, J., & Genabith, J. V. (2018). Neural machine translation for low-resource languages without parallel corpora. Machine translation, 32(1-2), 167-189. Search in Google Scholar

[13] Zhang, L. (2022). Design of new word retrieval algorithm for chinese-english bilingual parallel corpus. Mathematical Problems in Engineering, 2022. Search in Google Scholar

[14] Barnes, J., & Klinger, R. (2019). Embedding projection for targeted cross-lingual sentiment: model comparisons and a real-world study. Journal of Artificial Intelligence Research, 66. Search in Google Scholar

[15] Rivera-Acosta, M., Ruiz-Varela, J. M., Ortega-Cisneros, S., Rivera, J., Ramón Parra-Michel, & Mejia-Alvarez, P. (2021). Spelling correction real-time american sign language alphabet translation system based on yolo network and lstm. Electronics(9). Search in Google Scholar

[16] Pan, W. (2021). English machine translation model based on an improved self-attention technology. Scientific programming(Pt.14), 2021. Search in Google Scholar

[17] Linying, Z., & Qiang, S. (2017). Research on english machine translation based on the basic word analysis. Revista de la Facultad de Ingenieria, 32(16), 58-65. Search in Google Scholar

[18] Li, Z., Qu, D., Li, Y., Xie, C., & Chen, Q. (2020). A position weighted information based word embedding model for machine translation. International Journal on Artificial Intelligence Tools. Search in Google Scholar

[19] Wang, N., & Wang, M. (2017). Research on machine translation sentence pattern transformation method based on bp neural network. Revista de la Facultad de Ingenieria, 32(3), 693-700. Search in Google Scholar