CMS - Intelligent Machine Translation with Adaptation and AI
DOI:
https://doi.org/10.18034/abcjar.v10i2.693Keywords:
Adaptation, CMS, Machine Translation, Artificial IntelligenceAbstract
Machine translation, an emerging breakthrough, has changed translation. Dictionary-based machine translation, computer-aided translation, and neural machine translation with AI as its fundamental technology have progressed. However, NMT with AI has advanced machine translation. Many translation concerns still need to be solved. Language changes with context and dialect. Artificial intelligence will enable systems with adaptive algorithms to collaborate with humans to translate content more efficiently and well. The human translation should be revised, according to some. All in all, human progress fixes faults. Neural networks in machine translation ensure that adaptive frameworks can interpret like human translators. AI still needs help with language training and translation. Given the diversity of linguistic patterns and civilizations, they address clever machines; even with AI, handling human productivity seems unlikely. Machine translation is an important NLP topic that uses computers and adaptive systems to understand standard dialects. Neural machine translation (NMT) has become the standard in real-world MT frameworks. We begin this study with a broad assessment of NMT strategies and discuss architecture, decoding, and data analysis to improve content translation. We then summarize the most helpful expert resources and tools. We conclude with a discussion of future paths.
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References
Anastasiou, D., & Gupta, R. (2011). Comparison of Crowdsourcing Translation with Machine Translation. Journal of Information Science, 37(6), 237-238.
Bodepudi, A., Reddy, M., Gutlapalli, S. S., & Mandapuram, M. (2019). Voice Recognition Systems in the Cloud Networks: Has It Reached Its Full Potential? Asian Journal of Applied Science and Engineering, 8(1), 51–60. https://doi.org/10.18034/ajase.v8i1.12
Guo, J., Tan, X., He, D., Qin, T., Xu, L., and Liu, T. -Y. (2019). Non-autoregressive neural machine translation with enhanced decoder input. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, p. 3723–3730
Gutlapalli, S. S. (2016). Commercial Applications of Blockchain and Distributed Ledger Technology. Engineering International, 4(2), 89–94. https://doi.org/10.18034/ei.v4i2.653
Gutlapalli, S. S. (2017). An Early Cautionary Scan of the Security Risks of the Internet of Things. Asian Journal of Applied Science and Engineering, 6, 163–168. Retrieved from https://ajase.net/article/view/14
Gutlapalli, S. S., Mandapuram, M., Reddy, M., & Bodepudi, A. (2019). Evaluation of Hospital Information Systems (HIS) in terms of their Suitability for Tasks. Malaysian Journal of Medical and Biological Research, 6(2), 143–150. https://doi.org/10.18034/mjmbr.v6i2.661
Luong, T., Pham, H., and Manning, C. D. (2015). Effective approaches to attention-based neural machine translation, in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, (Lisbon, Portugal), pp. 1412–1421, Association for Computational Linguistics.
Mandapuram, M. & Hosen, M. F. (2018). The Object-Oriented Database Management System versus the Relational Database Management System: A Comparison. Global Disclosure of Economics and Business, 7(2), 89-96. https://doi.org/10.18034/gdeb.v7i2.657
Mandapuram, M. (2016). Applications of Blockchain and Distributed Ledger Technology (DLT) in Commercial Settings. Asian Accounting and Auditing Advancement, 7(1), 50–57. https://4ajournal.com/article/view/76
Mandapuram, M. (2017). Security Risk Analysis of the Internet of Things: An Early Cautionary Scan. ABC Research Alert, 5(3), 49–55. https://doi.org/10.18034/ra.v5i3.650
Mandapuram, M., Gutlapalli, S. S., Bodepudi, A., & Reddy, M. (2018). Investigating the Prospects of Generative Artificial Intelligence Asian Journal of Humanity, Art and Literature, 5(2), 167-174. https://doi.org/10.18034/ajhal.v5i2.659
Mandapuram, M., Gutlapalli, S. S., Reddy, M., Bodepudi, A. (2020). Application of Artificial Intelligence (AI) Technologies to Accelerate Market Segmentation. Global Disclosure of Economics and Business, 9(2), 141-150. https://doi.org/10.18034/gdeb.v9i2.662
Och, F. J., Tillmann, C., and Ney, H. (1999). Improved alignment models for statistical machine translation, in 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999.
Reddy, M., Bodepudi, A., Mandapuram, M., & Gutlapalli, S. S. (2020). Face Detection and Recognition Techniques through the Cloud Network: An Exploratory Study. ABC Journal of Advanced Research, 9(2), 103–114. https://doi.org/10.18034/abcjar.v9i2.660
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Copyright (c) 2021 Ruhul Amin, Mounika Mandapuram
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