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2021 | OriginalPaper | Buchkapitel

69. Machine Translation System Using Deep Learning for Punjabi to English

verfasst von : Kamal Deep, Ajit Kumar, Vishal Goyal

Erschienen in: Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences

Verlag: Springer Singapore

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Abstract

Machine Translation (MT) is ongoing research from the last decades. Research started with a simple word to word replacement from source (e.g. English) to target language (e.g. Hindi). Then research moved to statistical-based machine translation (SBMT) which is based on the parallel corpus. Now from the last few years, deep learning is used to develop an MT system. In this paper, an artificial neural network-based machine translation (ANMT) system is trained and tested for Punjabi to the English language. To train the proposed system a parallel corpus of Punjabi-English language is prepared and based on this corpus three models for Punjabi to English NMT system have been developed. In this work, the BLEU score is used to evaluate the performance of the system. The proposed system had shown a BLEU score of 36.98 for smaller sentences, 34.38 for medium sentences and 24.51 for large sentences using model 1, BLEU score of 36.62 for smaller sentences, 35.51 for medium sentences and 26.61 for large sentences using model 2, BLEU score of 60.68 for smaller sentences, 39.22 for medium sentences and 26.38 for large sentences using model 3.

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Literatur
3.
Zurück zum Zitat Choudhary H, Pathak AK, Saha RR, Kumaraguru P (2018) Neural machine translation for English–Tamil. In: Proceedings of the third conference on machine translation: shared task papers, pp 770–775 Choudhary H, Pathak AK, Saha RR, Kumaraguru P (2018) Neural machine translation for English–Tamil. In: Proceedings of the third conference on machine translation: shared task papers, pp 770–775
4.
Zurück zum Zitat Zhang J, Utiyama M, Sumita E, Neubig G, Nakamura S (2017) Improving neural machine translation through phrase-based forced decoding, arXiv preprint arXiv:1711.00309 Zhang J, Utiyama M, Sumita E, Neubig G, Nakamura S (2017) Improving neural machine translation through phrase-based forced decoding, arXiv preprint arXiv:1711.00309
5.
Zurück zum Zitat Mamta (2015) A review of various approaches used for machine translation. Int J Adv Res Comput Sci Manag Stud 3(2):108–113 Mamta (2015) A review of various approaches used for machine translation. Int J Adv Res Comput Sci Manag Stud 3(2):108–113
6.
Zurück zum Zitat Gupta S, Chatterjee N (2017) A hybrid approach using phrases and rules for Hindi to English machine translation. Int J Nat Lang Comput (IJNLC) 6(3):57–73CrossRef Gupta S, Chatterjee N (2017) A hybrid approach using phrases and rules for Hindi to English machine translation. Int J Nat Lang Comput (IJNLC) 6(3):57–73CrossRef
7.
Zurück zum Zitat Ahmadi S (2019) A rule-based kurdish text transliteration system. ACM Trans Asian Low-Resource Lang Inf Process (TALLIP) 18(2):1–8CrossRef Ahmadi S (2019) A rule-based kurdish text transliteration system. ACM Trans Asian Low-Resource Lang Inf Process (TALLIP) 18(2):1–8CrossRef
8.
Zurück zum Zitat Banik D, Ekbal A, Bhattacharyya P, Bhattacharyya S (2019) Assembling translations from multi-engine machine translation outputs. Appl Soft Comput 78:230–239CrossRef Banik D, Ekbal A, Bhattacharyya P, Bhattacharyya S (2019) Assembling translations from multi-engine machine translation outputs. Appl Soft Comput 78:230–239CrossRef
9.
Zurück zum Zitat Chaudhary JR, Patel AC (2018) Machine translation using deep learning: a survey. Int J Sci Res Sci Eng Technol 4(2):145–150 Chaudhary JR, Patel AC (2018) Machine translation using deep learning: a survey. Int J Sci Res Sci Eng Technol 4(2):145–150
10.
Zurück zum Zitat Vaswani A, Benigo S, Brevdo E, Chollet F, Gomez AN, Gouws S, Jones L, Kaiser Ł, Kalchbrenner N, Parmar N, Sepassi R, Shazeer N, Uszkoreit (2018) Tensor2Tensor for neural machine translation. arXiv preprint arXiv:1803.07416 Vaswani A, Benigo S, Brevdo E, Chollet F, Gomez AN, Gouws S, Jones L, Kaiser Ł, Kalchbrenner N, Parmar N, Sepassi R, Shazeer N, Uszkoreit (2018) Tensor2Tensor for neural machine translation. arXiv preprint arXiv:1803.07416
11.
Zurück zum Zitat Zhang Y, Vogel SE, Waibel AH (2004) Interpreting BLEU/NIST scores: how much improvement do we need to have a better system? In: Proceedings of 4th international conference on language resources and evaluation, pp 2051–2054 Zhang Y, Vogel SE, Waibel AH (2004) Interpreting BLEU/NIST scores: how much improvement do we need to have a better system? In: Proceedings of 4th international conference on language resources and evaluation, pp 2051–2054
12.
Zurück zum Zitat Sinha RMK (2003) AnglaHindi: an English to Hindi machine-aided translation system. In: MT summit IX Sinha RMK (2003) AnglaHindi: an English to Hindi machine-aided translation system. In: MT summit IX
13.
Zurück zum Zitat Oh JH, Choi KS, Isahara H (2006) A comparison of different machine transliteration models. J Artif Intell Res 27:119–151CrossRef Oh JH, Choi KS, Isahara H (2006) A comparison of different machine transliteration models. J Artif Intell Res 27:119–151CrossRef
14.
Zurück zum Zitat Singh U, Goyal V, Lehal GS (2016) Urdu to Punjabi machine translation: an incremental training approach. Int J Adv Comput Sci Appl 7(4):227–238 Singh U, Goyal V, Lehal GS (2016) Urdu to Punjabi machine translation: an incremental training approach. Int J Adv Comput Sci Appl 7(4):227–238
15.
Zurück zum Zitat Greenstein E, Penner D (2015) Japanese-to-English machine translation using recurrent neural networks. Stanford Deep Leaning, NLP Course, pp 1–7 Greenstein E, Penner D (2015) Japanese-to-English machine translation using recurrent neural networks. Stanford Deep Leaning, NLP Course, pp 1–7
20.
Zurück zum Zitat Klein G, Kim Y, Deng Y, Senellart J, Rush AM (2017) OpenMT: open-source toolkit for neural machine translation. In: Proceedings of ACL 2017, system demonstrations. Association for Computational Linguistics, Vancouver, Canada, pp 67–72 Klein G, Kim Y, Deng Y, Senellart J, Rush AM (2017) OpenMT: open-source toolkit for neural machine translation. In: Proceedings of ACL 2017, system demonstrations. Association for Computational Linguistics, Vancouver, Canada, pp 67–72
Metadaten
Titel
Machine Translation System Using Deep Learning for Punjabi to English
verfasst von
Kamal Deep
Ajit Kumar
Vishal Goyal
Copyright-Jahr
2021
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-7533-4_69

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