A Corpus-Based Auto-encoder-and-Decoder Machine Translation Using Deep Neural Network for Translation from English to Telugu Language

There is a huge demand for machine translation to design automated auto-encoders that can convert English to the Telugu language. Neural machine translation (NMT) is a new and effective technology that has significantly benefited traditional machine translation strategies. As a result, the primary g...

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Vydané v:SN computer science Ročník 4; číslo 4; s. 354
Hlavní autori: Mahanty, Mohan, Vamsi, Bandi, Madhavi, Dasari
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore Springer Nature Singapore 01.07.2023
Springer Nature B.V
Predmet:
Artificial neural networks
Coders
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data analysis
Data processing
Data Structures and Information Theory
Datasets
Enabling Innovative Computational Intelligence Technologies for IOT
English language
Information Systems and Communication Service
Language
Linguistics
Machine learning
Machine translation
Multilingual systems
Multilingualism
Neural networks
Original Research
Pattern Recognition and Graphics
Sentences
Software Engineering/Programming and Operating Systems
Syntax
Translating
Translations
Vision
India
Deep neural network
English-to-Telugu
Neural machine translation
Encoder-and-decoder
ISSN:2661-8907, 2662-995X, 2661-8907
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Popis
Shrnutí:There is a huge demand for machine translation to design automated auto-encoders that can convert English to the Telugu language. Neural machine translation (NMT) is a new and effective technology that has significantly benefited traditional machine translation strategies. As a result, the primary goal of this work is to propose an auto-encoder–decoder-based deep neural network for translating English to the Telugu language. Deep neural networks are used for learning, where input language is supplied to the neural network following data processing and analysis. The neural network then does auto-tuning, which helps to improve the translation quality. This proposed work produces target sentences in the Telugu language, resulting in a higher Bilingual Evaluation Understudy (BLEU) Score and a lower Word Error Rate (WER). The BLEU finds the similarity of the translated text by Machine Translation models with high-quality references and WER finds better accuracy in identifying the target sentence.
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-01678-4