Open Access

ARTICLE

Translation of English Language into Urdu Language Using LSTM Model

by Sajadul Hassan Kumhar¹, Syed Immamul Ansarullah², Akber Abid Gardezi³, Shafiq Ahmad⁴, Abdelaty Edrees Sayed⁴, Muhammad Shafiq^5,*

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:

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

Received 13 May 2022; Accepted 04 August 2022; Issue published 31 October 2022

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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