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

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, East West University, Dhaka, 1212, Bangladesh

    Md. Nawab Yousuf Ali & Md. Lizur Rahman

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Jyotismita Chaki

  3. Department of Computer Science and Engineering, JIS University, Kolkata, West Bengal, 700109, India

    Nilanjan Dey

  4. KC’s PAMI Research Lab, Computer Science, University of South Dakota, Vermillion, SD, 57069, USA

    K. C. Santosh

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  1. Md. Nawab Yousuf Ali
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  2. Md. Lizur Rahman
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  3. Jyotismita Chaki
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  4. Nilanjan Dey
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  5. K. C. Santosh
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Correspondence to K. C. Santosh.

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Ali, M.N.Y., Rahman, M.L., Chaki, J. et al. Machine translation using deep learning for universal networking language based on their structure. Int. J. Mach. Learn. & Cyber. 12, 2365–2376 (2021). https://doi.org/10.1007/s13042-021-01317-5

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