NLP-reliant Neural Machine Translation techniques used in smart city applications

  • Ritesh Kumar Dwivedi Department of Computer Science and Engineering, Sharda University, Greater Noida 201306, India
  • Parma Nand Department of Computer Science and Engineering, Sharda University, Greater Noida 201306, India
  • Om Pal Department of Computer Science, University of Delhi, New Delhi 201306, India
Article ID: 481
99 Views, 18 PDF Downloads
DOI: https://doi.org/10.59400/issc.v3i1.481
Keywords: NLP; Recurrent Neural Network (RNN); Neural Machine Translation (NMT)

Abstract

For smart city applications, Neural Machine Translation (NMT) methods based on Natural Language Processing (NLP) are crucial as they facilitate information sharing and communication among diverse populations. NLP techniques are used in many domains related to smart cities, such as development and research, business, industries, media, healthcare, and residences and communities. The majority of people in India communicate using their regional languages. The majority of applications used by users in smart cities will mostly accept English as input. These people will be able to interact with these smart city devices in their native tongues more effectively with the help of effective machine translation. Just 10% of Indians use English as their primary language of communication; there are 22 official regional languages in India. So, there is a requirement for better machine translation using natural language processing (NLP). Natural language processing for Indian regional languages has a very long way to go until it surpasses the abilities of existing rich NLP applications and techniques for the English language. Machine Translation is a technique of Natural Language Processing (NLP) that provides better inter-lingual communication. For low-resourced Indian languages, effective machine translation systems became important for establishing proper communication. Machine transliteration is a technique to convert source language into target language using a machine. The developed system takes the English language as input and then applies machine translation techniques to translate the source language into multiple languages using a trained RNN model and a multilingual search model that search the input word across all the datasets and generate the output into other Indian languages such as Hindi and Tamil. Our approach achieves top performance for the English-Hindi language pair and comparable results for other cases.

References

[1]Soumyadeep K, Sayantan P, Santanu P. A deep learning based approach to transliteration. In: Proceedings of the Seventh Named Entities Workshop. Association for Computational Linguistics. 2018. pp. 79–83.

[2]Harish BS, Rangan RK. A comprehensive survey on Indian regional language processing. SN Applied Sciences. 2020; 2(7). doi: 10.1007/s42452-020-2983-x

[3]Kunchukuttan A. An Introduction to Machine Translation & Transliteration. Available online: www.cse.iitb.ac.in/~anoopk (accessed on 19 June 2023).

[4]Vidya PV, Raj PCR, Jayan V. Web Page Ranking Using Multilingual Information Search Algorithm - A Novel Approach. Procedia Technology. 2016; 24: 1240-1247. doi: 10.1016/j.protcy.2016.05.102

[5]Narayan R, Singh VP, Chakraverty S. Quantum Neural Network Based Machine Translator for Hindi to English. The Scientific World Journal. 2014; 2014: 1-8. doi: 10.1155/2014/485737

[6]Sheshadri SK, Gupta D, Costa-Jussà MR. A Voyage on Neural Machine Translation for Indic Languages. Procedia Computer Science. 2023; 218: 2694-2712. doi: 10.1016/j.procs.2023.01.242

[7]Islam SI, Indika Devi MI. A Study on Various Applications of NLP Developed for North-East Languages.

[8]Bhattacharyya P, Murthy H, Ranathunga S, et al. Indic language computing. Communications of the ACM. 2019; 62(11): 70-75. doi: 10.1145/3343456

[9]Godase A, Govilkar S. Machine Translation Development for Indian Languages and its Approaches. International Journal on Natural Language Computing. 2015; 4(2): 55-74. doi: 10.5121/ijnlc.2015.4205

[10]Khan A, Sarfaraz A. RNN-LSTM-GRU based language transformation. Soft Computing. 2019; 23(24): 13007-13024. doi: 10.1007/s00500-019-04281-z

[11]Mallick R, Susan S, Agrawal V, et al. Context- and sequence-aware convolutional recurrent encoder for neural machine translation. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing; 22-26 March 2021; Online Conference. pp. 853-856. doi: 10.1145/3412841.3442099

[12]Manogaran G, Qudrat-Ullah H, Xin Q, et al. Special Issue on Deep Structured Learning for Natural Language Processing. ACM Transactions on Asian and Low-Resource Language Information Processing. 2021; 20(1): 1-2. doi: 10.1145/3436206.

[13]Philip J, Siripragada S, Namboodiri VP, et al. Revisiting Low Resource Status of Indian Languages in Machine Translation. In: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD); 2-4 January 2021; Bangalore, India. pp. 178-187. doi: 10.1145/3430984.3431026

[14]Ramesh A, Parthasarathy VB, Haque R, et al. Comparing Statistical and Neural Machine Translation Performance on Hindi-To-Tamil and English-To-Tamil. Digital. 2021; 1(2): 86-102. doi: 10.3390/digital1020007

[15]Singh V pal, Kumar P. Word sense disambiguation for Punjabi language using deep learning techniques. Neural Computing and Applications. 2019; 32(8): 2963-2973. doi: 10.1007/s00521-019-04581-3

[16]Srivastava S, Govilkar S. A Survey on Paraphrase Detection Techniques for Indian Regional Languages. International Journal of Computer Applications. 2017; 163(9): 42-47. doi: 10.5120/ijca2017913757

[17]Vathsala MK, Holi G. RNN based machine translation and transliteration for Twitter data. International Journal of Speech Technology. 2020; 23(3): 499-504. doi: 10.1007/s10772-020-09724-9

[18]Yu Z, Yu Z, Guo J, et al. Efficient Low-Resource Neural Machine Translation with Reread and Feedback Mechanism. ACM Transactions on Asian and Low-Resource Language Information Processing. 2020; 19(3): 1-13. doi: 10.1145/3365244

[19]Zhou L, Zhang J, Kang X, et al. Deep Neural Network--based Machine Translation System Combination. ACM Transactions on Asian and Low-Resource Language Information Processing. 2020; 19(5): 1-19. doi: 10.1145/3389791

Published
2023-10-02
How to Cite
Dwivedi, R. K., Nand, P., & Pal, O. (2023). NLP-reliant Neural Machine Translation techniques used in smart city applications. Information System and Smart City, 3(1), 481. https://doi.org/10.59400/issc.v3i1.481
Issue
Vol. 3 No. 1 (2023)
Section
Article

Copyright (c) 2023 Ritesh Kumar Dwivedi, Parma Nand, Om Pal

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it for any purpose, even commercially, under the condition that the authors are given credit. With this license, authors hold the copyright.