Translation of English Language into Urdu Language Using LSTM Model

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 transl...

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Bibliographic Details
Published in:Computers, materials & continua Vol. 74; no. 2; pp. 3899 - 3912
Main Authors: Hassan Kumhar, Sajadul, Immamul Ansarullah, Syed, Abid Gardezi, Akber, Ahmad, Shafiq, Edrees Sayed, Abdelaty, Shafiq, Muhammad
Format: Journal Article
Language:English
Published: Henderson Tech Science Press 2023
Subjects:
Automation
Decoding
Encoders-Decoders
English language
Language
Machine translation
Structural analysis
Translating
ISSN:1546-2226, 1546-2218, 1546-2226
Online Access:Get full text
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Summary: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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ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2023.032290