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Machine translation using deep learning for universal networking language based on their structure

delete2021-04-27
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PRE
AI
M
Md. Nawab Yousuf Ali
M
Md. Lizur Rahman
J
Jyotismita Chaki
N
Nilanjan Dey
K
KC Santosh *
DOI:10.1007/s13042-021-01317-5delete
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摘要

摘要

En 中文
This paper presents a deep learning-based machine translation (MT) system that translates a sentence of subject-object-verb (SOV) structured language into subject-verb-object (SVO) structured language. This system uses recurrent neural networks (RNNs) and Encodings. Encode embedded RNNs generate a set of numbers from the input sentence, where the second RNNs generate the output from these sets of numbers. Three popular datasets of SOV structured language i.e., EMILLE corpus, Prothom-Alo corpus and Punjabi Monolingual Text Corpus ILCI-II are used as two different case-study to validate. In our experimental case-study 1, for the EMILLE corpus and Prothom-Alo corpus dataset, we have achieved 0.742, 4.11 and 0.18, respectively as Bilingual Evaluation Understudy (BLEU), NIST (metric) and tertiary entrance rank scores. Another case-study for Punjabi Monolingual Text Corpus ILCI-II dataset achieved a BLEU score of 0.75. Our results can be compared with the state-of-the-art results.
Keyword:
Machine translation
Deep learning
Recurrent neural network
Encodings
Sequence-to-sequence learning
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期刊

International Journal of Machine Learning and Cybernetics 封面图
International Journal of Machine Learning and Cybernetics
IF:
2.7
论文数:
3.2K
被引数:
5.6K

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vit vellore
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east west university bangladesh
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university of south dakota
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