Instant Multilingual Translation using Deep Learning Techniques
DOI:
https://doi.org/10.65204/Abstract
The objective of this paper is to a void the difficulty of analysis and the high amount of time that is required by conventional translation methods and reduce the validation loos by simply using training data when augmenting dat
References
Byungsoo Kim et al., « Lagrangian Neural Style Transfer for Fluids », ACM Transactions on Graphics 39.4 (2020)
Scott Mayer McKinney et al., « International evaluation of an AI system for breast cancer screening », Nature 577.7788 (2020)
Lindsay, G. Convolutional neural networks as a model of the visual system: Past, present, and future. J. Cogn. Neurosci. 1–15 (2020).
Lee, W., Kim, D., Hong, S. & Lee, H. High-fidelity synthesis with disentangled representation. arxiv (2020).
Chang, L., Egger, B., Vetter, T. & Tsao, D. Y. What computational model provides the best explanation of face representations in the primate brain? bioRxiv (2020).
Duan, S. et al. Unsupervised model selection for variational disentangled representation learning. ICLR (2020).
Abdul Rauf, S. and Yvon, F. (2024) ‘Translating scientific abstracts in the bio-medical domain with structure-aware models’, Comput. Speech Lang., Vol. 87, DOI: 10.1016/j.csl.2024.101623.
Ahda, F.A., Wibawa, A.P., Prasetya, D.D. and Sulistyo, D.A. (2024) ‘Comparison of Adam optimization and RMSprop in Minangkabau-Indonesian bidirectional translation with neural machine translation’, International Journal on Informatics Visualization, Vol. 8, No. 1, DOI: 10.62527/joiv.8.1.1818.
An, Z., Wu, J., Yang, M., Zhou, D.L. and Zeng, B. (2024) ‘Unified quantum state tomography and Hamiltonian learning: A language-translation-like approach for quantum systems’, Phys. Rev. Appl., Vol. 21, No. 1, DOI: 10.1103/PhysRevApplied.21.014037.
Anderson, R., Scala, C., Samuel, J., Kumar, V. and Jain, P. (2024) ‘Are emotions conveyed across machine translations? Establishing an analytical process for the effectiveness of multilingual sentiment analysis with Italian text’, Journal of Big Data and Artificial Intelligence, Vol. 2, No. 1, DOI: 10.54116/jbdai.v2i1.30.
Belete, M.D., Salau, A.O., Alitasb, G.K. and Bezabh, T. (2024) ‘Contextual word disambiguates of Ge’ez language with homophonic using machine learning’, Ampersand, Vol. 12, DOI:10.1016/j.amper.2024.100169.
Chandrakala, C.B., Bhardwaj, R. and Pujari, C. (2024) ‘An intent recognition pipeline for conversational AI’, International Journal of Information Technology, Vol. 16, No. 2, Singapore,DOI:10.1007/s41870-023-016428.
Guimarães, N., Campos, R. and Jorge, A. (2024) ‘Pre-trained language models: What do they know?’, Wiley Interdiscip Rev. Data Min. Knowl. Discov., Vol. 14, No. 1, DOI: 10.1002/ widm.1518.
Han, L., Gladkoff, S., Erofeev, G., Sorokina, I., Galiano, B. and Nenadic, G. (2024) ‘Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning’, Front. Digit Health, Vol. 6, DOI: 10.3389/fdgth. 2024.1211564.
Jing, X. (2024) ‘Automatic recognition of machine English translation errors using fuzzy set algorithm’, Soft Comput., DOI: 10.1007/s00500-023-09543-5.
Ju, Z., Xin, Y. and Ye, M. (2024) ‘Machine translation of classical Chinese based on unigram segmentation transformer framework’, Applied and Computational Engineering, Vol. 37, No. 1, DOI: 10.54254/2755-2721/37/20230465.
Karabayeva, I. and Kalizhanova, A. (2024) ‘Evaluating machine translation of literature through rhetorical analysis’, Journal of Translation and Language Studies, Vol. 5, No. 1, DOI: 10.48185/jtls.v5i1.962.
Kaur, K. and Chauhan, S. (2024) ‘A comparative analysis of lexical-based automatic evaluation metrics for different Indic language pairs’, Journal of Autonomous Intelligence, Vol. 7, No. 4, DOI: 10.32629/jai.v7i4.1393.
Kjell, O.N.E., Kjell, K. and Schwartz, H.A. (2024) ‘Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment’, DOI: 10.1016/j.psychres.2023.115667.
Luo, S., Ivison, H., Han, S.C. and Poon, J. (2024) ‘Local interpretations for explainable natural language processing: a survey’, ACM Comput. Surv., Vol. 56, No. 9, DOI: 10.1145/3649450.
Macas, M., Wu, C. and Fuertes, W. (2024) ‘Adversarial examples: a survey of attacks and defenses in deep learning-enabled cybersecurity systems’, DOI: 10.1016/j.eswa.2023.122223.
Mahdi, M.G., Sleem, A. and Elhenawy, I. (2024) ‘Deep learning algorithms for Arabic optical character recognition: a survey’, Multicriteria Algorithms with Applications, Vol. 2, DOI: 10.61356/j.mawa.2024.26861.
Mi, C. and Xie, S. (2024) ‘Language relatedness evaluation for multilingual neural machine translation’, Neurocomputing, Vol. 570, DOI: 10.1016/j.neucom.2023.127115.
Nam, W. and Jang, B. (2024) ‘A survey on multimodal bidirectional machine learning translation of image and natural language processing’, DOI: 10.1016/j.eswa.2023.121168.
Ni, P., Okhrati, R., Guan, S. and Chang, V. (2024) ‘Knowledge graph and deep learning-based text-to-graphql model for intelligent medical consultation chatbot’, Information Systems Frontiers, Vol. 26, No. 1, DOI: 10.1007/s10796-022-10295-0.
Oliveira, E.E., Rodrigues, M., Pereira, J.P., Lopes, A.M., Mestric, I.I. and Bjelogrlic, S. (2024) ‘Unlabeled learning algorithms and operations: overview and future trends in defense sector’, Artif. Intell. Rev., Vol. 57, No. 3, DOI: 10.1007/s10462-023-10692-0.
Patle, R. and Kalra, G. (2024) ‘enhancing natural language processing (NLP) through VIKOR method: a comprehensive approach for improved computational linguistics’, Computer Science, Engineering and Technology, Vol. 2, No. 1, DOI: 10.46632/cset/2/1/4.
Pradhan, A. and Yajnik, A. (2024) ‘Parts-of-speech tagging of Nepali texts with Bidirectional LStM, conditional random fields and HMM’, Multimed. Tools Appl., Vol. 83, No. 4, DOI: 10.1007/s11042-023-15679-1.
Shang, F. and Li, Y. (2024) ‘Exploring english long sentence translation methods by applying natural language processing techniques’, Applied Mathematics and Nonlinear Sciences, Vol. 9, No. 1, DOI: 10.2478/amns.2023.2.01352.
Shenbagaraj, A. and Iyer, S. (2024) ‘An intelligent system for automated translation of videos from English to native language applying artificial intelligence techniques for adaptive elearning’, International Journal of Intelligent Systems and Applications in Engineering, Vol. 12, No. 3, p.2024.
Sohrab, M.G., Asada, M., Rikters, M. and Miwa, M. (2024) ‘BERT-NAR-BERT: a non-autoregressive pre-trained sequence-to-sequence model leveraging BERT checkpoints’, IEEE Access, Vol. 12, DOI: 10.1109/ACCESS.2023.3346952.
Sunar, A.S. and Khalid, M.S. (2024) ‘Natural language processing of student’s feedback to instructors: a systematic review’, IEEE Transactions on Learning Technologies, Vol. 17, DOI: 10.1109/TLT.2023.3330531.
Wei, C., Wang, Y-C., Wang, B. and Kuo, C-C.J. (2024) ‘An overview of language models: recent developments and outlook’, APSIPA Trans. Signal. Inf. Process, Vol. 13, No. 2, DOI: 10. 1561/116.00000010.
Zemni, B., Zitouni, M., Bouhadiba, F. and Almutairi, M. (2024) ‘A comparative study of the lexical ambiguity of Arabic, English, and French in natural language processing’, Journal of Intercultural Communication, Vol. 24, No. 1, DOI: 10.36923/jicc.v24i1.171.
Zemni, B., Zitouni, M., Bouhadiba, F. and Almutairi, M. (2024) ‘On ambiguity in the arabic language: scrutinizing translation issues through machine translation from English and French into Arabic’, Journal of Intercultural Communication, DOI: 10.36923/jicc.v24i1.171.
Zhang, W., Bao, Y. and Xu, W. (2024) ‘Design and implementation of natural language processing machine translation system based on Seq2Seq model’, in Frontiers in Artificial Intelligence and Applications, DOI: 10.3233/FAIA231215.