Bi-Lingual Machine Translation Approach using Long Short–Term Memory Model for Asian Languages

A Shalini Divya Prasanna; C Beulah Christalin Latha

doi:10.17485/IJST/v16i18.176

Article

Bi-Lingual Machine Translation Approach using Long Short–Term Memory Model for Asian Languages

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i18.176

Year: 2023, Volume: 16, Issue: 18, Pages: 1357-1364

Original Article

Bi-Lingual Machine Translation Approach using Long Short–Term Memory Model for Asian Languages

A Shalini Divya Prasanna^1*, C Beulah Christalin Latha²

¹Research Scholar, Department of Digital Sciences, Karunya Institute of Technology and Sciences, Coimbatore, 641114, Tamil Nadu, India
²Associate Professor, Department of Digital Sciences, Karunya Institute of Technology and Sciences,, Coimbatore, 641114, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:23 January 2023, Accepted Date:01 April 2023, Published Date:08 May 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To develop an appropriate machine translation model for translating text from English to Tamil. Methods: The proposed work uses a Gated Recurrent Unit (GRU) Long Short-Term Memory (LSTM) model. The Repeat Vector function is used for fitting both the decoder and encoder parts of the network model. Adam optimizer is used because of its faster execution and less consumption of memory. It mainly uses the text corpora which are available in the Internet repository namely Technology Development for Indian Languages (TDIL), Linguistic Data Consortium for Indian Languages (LDCIL), Kaggle, and Ishikahooda. Findings: The motivation for the proposed work emerged from identifying the regional language Tamil as one of the less frequently used languages in the existing translation systems. The Tamil Character Set is one of the challenging factors for the existence of fewer such translation systems. The proposed system produces a BLEU score of 0.9, a Meteor score of 0.98, a TER score of 0.5, a WER score of 20%, an Accuracy rate of 5 (in a 5-point grading scale), and an Adequacy rate of 5 (on a 5-point grading scale) which are significantly better than the existing systems. Novelty: The space complexity of the proposed LSTM-based English Tamil Translator is fine-tuned to 256 units of memory using Adam optimizer for achieving less storage consumption. The number of layers is optimized for reducing the execution time. Unicode Transformation Format (UTF-8) encoding is used to incorporate Tamil language characters. This work has been implemented with a wide range of sentences counted to several thousand. LSTM-based English Tamil Translator is helpful for bilingual learners who are learning specifically Tamil language.

Keywords: Machine translation; Deep Learning; LSTM; English; Tamil

References

Syed A, Abdul W. Machine Translation System Using Deep Learning for English to Urdu. 2022. Available from: https://doi.org/10.1155/2022/7873012
Laith A, Jinglan Z, Humaidi AJ, Ayad AD, Ye D, Omran AS, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data. 2021;8:53. Available from: https://doi.org/10.1186/s40537-021-00444-8
Antonio HB, Boris HF, David T, Borja NC. A Systematic Review of Deep Learning Approaches to Educational Data Mining. Hindawi Complexity. 2019. Available from: https://doi.org/10.1155/2019/1306039
Kumar MA, Premjith B, Singh S, Rajendran S, Soman KP. An Overview of the Shared Task on Machine Translation in Indian Languages (MTIL) – 2017. Journal of Intelligent Systems. 2019;28(3):455–464. Available from: https://www.degruyter.com/document/doi/10.1515/jisys-2018-0024/html
Mandke A, Litake O, Kadam D. Analyzing Architectures for Neural Machine Translation using Low Computational Resources. International Journal on Natural Language Computing. 2021;10(5):9–16. Available from: https://doi.org/10.5121/ijnlc.2021.10502
Karyukin V, Rakhimova D, Karibayeva A, Turganbayeva A, Turarbek A. The neural machine translation models for the low-resource Kazakh–English language pair. PeerJ Computer Science. 2023;9:e1224. Available from: https://doi.org/10.7717/peerj-cs.1224
Akshai R, Venkatesh BP, Rejwanul H, AW. Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. 2021. Available from: https://doi.org/10.3390/digital1020007
Premjith B, Soman KP. Deep Learning Approach for the Morphological Synthesis in Malayalam and Tamil at the Character Level. ACM Transactions on Asian and Low-Resource Language Information Processing. 2021;20(6):1–17. Available from: https://doi.org/10.1145/3457976
Zhixing T, Shuo W, Zonghan Y, Gang C, Xuancheng H, Maosong S, et al. Neural machine translation: A review of methods, resources, and tools. 2020. Available from: https://doi.org/10.48550/arXiv.2012.155
Vijaay KU, Mahesh N, Karthikeyan B, Rama S. Attention based Neural Machine Translation for English-Tamil Corpus. International Research Journal of Engineering and Technology (IRJET). 2020;p. 4. Available from: https://www.irjet.net/archives/V7/i4/IRJET-V7I4703.pdf
Kandimalla A, Lohar P, Maji SK, Way ASK. Improving English-to-Indian Language Neural Machine Translation Systems. Information. 13(5):245. Available from: https://doi.org/10.3390/info13050245
Harish BS, Rangan RK. A comprehensive survey on Indian regional language processing. SN Applied Sciences. 2020;2(7). Available from: https://doi.org/10.1007/s42452-020-2983-x
Narayan R, Chakraverty S, Singh VP. Quantum neural network based machine translator for English to Hindi. Applied Soft Computing. 2016;38:1060–1075. Available from: https://doi.org/10.1016/j.asoc.2015.08.031
Amarnath P, Partha P. Neural Machine Translation for Indian Languages. Journal of Intelligent Systems. De Gruyter. 2019;28(3):465–477. Available from: https://doi.org/10.1515/jisys-2018-0065
Raphael R, Benjamin M, Raj D, Atushi F, Masao U, Eiichiro S. Extremely low-resource neural machine translation for Asian languages. 2020. Available from: https://doi.org/10.1007/s10590-020-09258-6
Yanping Y. Translation Mechanism of Neural Machine Algorithm for Online English Resources. Hindawi Complexity. 2021. Available from: https://doi.org/10.1155/2021/5564705
Andrabi S, Wahid A. A review of machine translation for South Asian low resource languages. Turkish Journal of Computer and Mathematics Education. 2021;12(5):1134–1147. Available from: https://pdfs.semanticscholar.org/d9d9/9880f50dc1cf7ff58a40869efdfa9e723d09.pdf
Ramesh A, Parthasarathy VB, Haque R, Way A. Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. Digital. 2021;1(2):86–102. Available from: https://doi.org/10.3390/digital1020007

Copyright

© 2023 Prasanna & Latha. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)