An Intelligent System for Automated translation of Videos from English to Native Language applying Artificial Intelligence Techniques for Adaptive eLearning
Keywords:
Machine translation, Adaptive eLearning, Machine learning, Animated videos translationAbstract
Generally providing the content in one’s own language is helpful for students to learn better in their mother tongue. Using the mother tongue for learning in Adaptive eLearning has been particularly effective in terms of understanding and engagement level. So, translation to one’s mother tongue becomes one of the most important phases in the Adaptive eLearning development process. But translation of eLearning videos has been one of the toughest jobs to do due to the involvement of various stake holders and manual coordination work involved. The entire translation process of the videos had been tried earlier by involving mostly manual work and found to be tiresome. After the recent evolution of Machine learning and Natural language processing techniques, it is feasible now to do the translation by inculcating intelligent techniques in the process. We have implemented machine learning techniques while translating the videos. The results show that this intelligent automation drastically reduces the time to translate the videos by involving less manpower. The methodology help us to achieve optimised utilization of resources and production time.
Downloads
References
Shenbagaraj R., Iyer S. (2021) A Smart Education Solution for Adaptive Learning with Innovative Applications and Interactivities. In: Hassanien A.E., Bhattacharyya S., Chakrabati S., Bhattacharya A., Dutta S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1300. Springer, Singapore. https://doi.org/10.1007/978-981-33-4367-2_65
AWS Machine Learning Blog, aws.amazon.com/blogs/machine-learning/create-video-subtitles-with-translation-using-machine-learning/, last accessed 2021/07/10.
IBMWebsite, https://www.ibm.com/ibm/history/ibm100/us/en/icons/translation/
Towards data science website,
https://towardsdatascience.com/evolution-of-machine-translation-5524f1c88b25
R.M.K. Sinha, K. Sivaramam, Aditi Agrawal, Renu Jain, Rakesh Srivastava, Ajai Jain, “ANGLABHARTI: A Multilingual Machine Aided Translation Project on Translation from English to Indian Language”, Proc. IEEE, 1995. Available: IEEE https://ieexplore.iee.org/stamp/stamp.jsp?tp=&arnumber=538002&tag=1.
Akshar Bharati, Vineet Chaitanya, Amba P.Kulkarni and Rajeev Sangal, “ ANUSAARAKA: Machine Translation in Stages”, A Quarterly in Artificial Intelligence, Vol 10, No. 3, July 1997.
Brown et al, “A Statistical Approach to Machine Translation”, Computational Linguistics Volume 16, November 2, June 1990
Peter F.Brown, Stephen A.Della Pietra, Vincent J.Della Pietra, Stephen A.Della Pietra, Robert L.Mercer, IBM T.J Watson Research Center, “The Mathematics of Statistical Machine Translation: Parameter Estimation”, 1993, Association of Computational Linguistics.
Stephan Vogel, Hermann Ney and Christoph Tillmann, “HMM-Based Word Alignment in Statistical Translation”, COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics.
Franz Josef Och, Christoph Till mann, and Hermann Ney, “ Improved Alignment Models for Statistical Machine Translation, 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora.
Kenji Yamada and Kevin Knight, “A Syntax-based Statistical Translation Model”, Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics.
Philip Koehn, Franz J.Och, Daniel Marcu, “ Statistical Phrase-Based Translation” , Proceedings of the 2003 Human Langauge Technology Conference of the North American Chapter of the Association for Computational Linguistics.
David Chiang, “ A Hierarchical Phrase-Based Model for Statistical Machine Translation”, University of Maryland, College Park, MD
Nal Kalchbrenner and Phil Blunson, “Recurrent Contuinous Translation Models”, Proceedings of the 2013 Conference on Empirical Methods on Natural Language Processing.
IIya Sutskever, Oriol Vinyals and Quoc V.Le, “Sequence to Sequence Learning with Neural Networks, Google, Computation and Language, Computer Science, Cornell University
Dzmitry Bahdanau and KyungHyun Cho and Yoshua Bengio, “Neural Machine Translation by Jointly learning to Align and Translate”, Published as a conference paper at ICLR 2015.
Minh-Thang Luong, Hieu Pham and Christoper D.Manning, “ Effective Approaches to Attention-based Neural Machine Translation”, CSE Dept, Stanford University.
Rafal Jozefowicz et al, “ Exploring the Limits of Language Modeling”, Google Brain.
Ondrej Biojar et al, “Results of the WMT16 Metrics Shared Task”, Proceedings of the First Conference on Machine Translation:Volume 2, Shared Task Papers.
Mary Priya Sebastian, Sheena Kurian and G. Santhosh Kumar , “ A Framework of Statistical Machine Translator from English to Malayalam,”, Proc. 1st Amrita ACMW Celebration on Women in Computing, September, 2010.
Remya Rajan, Remya Sivan, Remya Srinivasan, K.P.Somam, “Rule Based Machine Translation from English to Malayalam”, International Conference on Advances in Computing, Control and Telecommunication Technologies, 2009.
Rinju O.R, Rajeev R.R, Reghu Raj P.C, Elizabeth Sherly, “ Morphological Analyzer for Malayalam: Probabilistic Method Vs Rule Based Method”, International Journal of Computational Linguistics and Natural Language Processing, ISSN 2279-0756, vol. 2, Issue 10, October 2013.
Keerthi Lingam, E. Rama Lakshmi and L.Ravi Teja, “Rule-based Machine Translation from English to Telugu with Emphasis on Prepositions”, International Conference on Networks & Soft Computing, 2014, Available [Online] IEEE: https://ieeexplore-ieee-org.libaccess.sjlibrary.org/stamp/stamp.jsp?tp=&anumber=6906669
D S Sharma, R Sangal and J D Pawar, Proc of the 11th Intl. Conference on Natural Language Processing, pages 167-176, Goa, India, December 2014.
Srikar Kashyap Pulipaka et al (2019) , Machine Translation of English Videos to Indian Regional Languages using Open Innovation, IEEE International Symposium on Technology in Society (ISTAS) Proceedings Miriam Cunnigham and Paul Cunnigham (Eds) ISBN:978-1-7281-5480-0
Steven Martin, Thomas Wood, María Fernández, Maria Hernandez, .María García. Machine Learning for Educational Robotics and Programming. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/179
Mahajan, R. ., Patil, P. R. ., Potgantwar, A. ., & Bhaladhare, P. R. . (2023). Novel Load Balancing Optimization Algorithm to Improve Quality-of-Service in Cloud Environment. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 57–64. https://doi.org/10.17762/ijritcc.v11i2.6110
Anupong, W., Azhagumurugan, R., Sahay, K. B., Dhabliya, D., Kumar, R., & Vijendra Babu, D. (2022). Towards a high precision in AMI-based smart meters and new technologies in the smart grid. Sustainable Computing: Informatics and Systems, 35 doi:10.1016/j.suscom.2022.100690
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
