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Translation of English Language into Urdu Language Using LSTM Model

Sajadul Hassan Kumhar1, Syed Immamul Ansarullah2, Akber Abid Gardezi3, Shafiq Ahmad4, Abdelaty Edrees Sayed4, Muhammad Shafiq5,*

1 SSSUTMS, Sehore, 466001, India
2 Department of Computer Science, GDC Sumbal, J&K, 193502, India
3 Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan
4 Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
5 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea

* Corresponding Author: Muhammad Shafiq. Email: email

Computers, Materials & Continua 2023, 74(2), 3899-3912. https://doi.org/10.32604/cmc.2023.032290

Abstract

English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation. In order to make knowledge available to the masses, there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion. Machine translation has achieved this goal with encouraging results. When decoding the source text into the target language, the translator checks all the characteristics of the text. To achieve machine translation, rule-based, computational, hybrid and neural machine translation approaches have been proposed to automate the work. In this research work, a neural machine translation approach is employed to translate English text into Urdu. Long Short Term Short Model (LSTM) Encoder Decoder is used to translate English to Urdu. The various steps required to perform translation tasks include preprocessing, tokenization, grammar and sentence structure analysis, word embeddings, training data preparation, encoder-decoder models, and output text generation. The results show that the model used in the research work shows better performance in translation. The results were evaluated using bilingual research metrics and showed that the test and training data yielded the highest score sequences with an effective length of ten (10).

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Cite This Article

APA Style
Kumhar, S.H., Ansarullah, S.I., Gardezi, A.A., Ahmad, S., Sayed, A.E. et al. (2023). Translation of english language into urdu language using LSTM model. Computers, Materials & Continua, 74(2), 3899-3912. https://doi.org/10.32604/cmc.2023.032290
Vancouver Style
Kumhar SH, Ansarullah SI, Gardezi AA, Ahmad S, Sayed AE, Shafiq M. Translation of english language into urdu language using LSTM model. Comput Mater Contin. 2023;74(2):3899-3912 https://doi.org/10.32604/cmc.2023.032290
IEEE Style
S.H. Kumhar, S.I. Ansarullah, A.A. Gardezi, S. Ahmad, A.E. Sayed, and M. Shafiq "Translation of English Language into Urdu Language Using LSTM Model," Comput. Mater. Contin., vol. 74, no. 2, pp. 3899-3912. 2023. https://doi.org/10.32604/cmc.2023.032290



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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