Development of English-Hindi Interactive Machine Translation

Sharma, Nidhi; Seth, Shambhavi; Syed, Mehvish; Jain, Monika; Bharti, Nivedita

doi:10.35940/ijeat.a1532.109119

Cited by 1 publication

(1 citation statement)

References 46 publications

(49 reference statements)

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“…CBR is used to select Urdu as a training method for introductory English sentences. Sharma et al [16] proposed a statistical machine translation model based on a Hindi-English bilingual corpus, which uses multiple algorithms such as independent location rate, translation error rate, word error rate, and n-gram to achieve translation from English to Hindi translate. Jawaid et al [17] proposed a language-based statistical translation model from English to Urdu.…”

Section: Introductionmentioning

confidence: 99%

Translation of English Language into Urdu Language Using LSTM Model

Kumhar¹,

Ansarullah²,

Gardezi³

et al. 2023

Computers, Materials &Amp; Continua

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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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Section: Introductionmentioning

confidence: 99%