Phrase Based and Neural Network Translation for Text Transliteration from Arabic to Indonesia

Alvian Burhanuddin, Ahmad Latif Qosim, Rizqi Amaliya


Abstract- Transliteration is one solution to overcome the inability to read and write Arabic in Indonesia. However, this transliteration has many different versions in reality. The many differences in transliteration versions make it difficult for people to understand and pronounce the Arabic sentence. So there needs to be an approach to overcome the problem of these differences. The data mining approach can be used as an option to reduce these differences. In this study, the researcher found that automatic transliteration based on the data mining model had a reasonably good BLEU value.


Transliteration; data mining; Indonesian language;

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S. Indonesia, “badan pusat statistik,” BPS-Stat. Indones., 2018.

I. R. Al-Faruqi, Toward Islamic English. International Institute of

Islamic Thought (IIIT), 1986.

N. F. Ahmad, “Problematika Transliterasi Aksara Arab-Latin:

Studi Kasus Buku Panduan Manasik Haji dan Umrah,” Nusa J. Ilmu

Bhs. Dan Sastra, vol. 12, no. 1, pp. 126–136, 2017.

M. Musadad, “Alquran transliterasi latin dan problematikanya

dalam ma-syarakat muslim denpasar,” SUHUF, vol. 10, no. 1, pp.

–209, 2017.

T. R. K. B. Indonesia, “Kamus Bahasa Indonesia,” Jkt. Pus. Bhs.

Dep. Pendidik. Nas., vol. 725, 2008.

K. B. M. Agama, M. Pendidikan, and K. R. Indonesia, “Nomor 158

Tahun 1987 dan Nomor 0543 b.” Departemen Agama, Jakarta, 1987.

I. Guellil, F. Azouaou, M. Abbas, and S. Fatiha, “Arabizi

transliteration of Algerian Arabic dialect into modern standard

Arabic,” 2017.

M. S. H. Ameur, F. Meziane, and A. Guessoum, “Arabic machine

transliteration using an attention-based encoder-decoder model,”

Procedia Comput. Sci., vol. 117, pp. 287–297, 2017.

A. Masmoudi, M. E. Khmekhem, M. Khrouf, and L. H. Belguith,

“Transliteration of arabizi into arabic script for tunisian dialect,” ACM

Trans. Asian Low-Resour. Lang. Inf. Process. TALLIP, vol. 19, no. 2,

pp. 1–21, 2019.

A. Lopez, “Statistical machine translation,” ACM Comput. Surv.

CSUR, vol. 40, no. 3, pp. 1–49, 2008.

P. Koehn et al., “Moses: Open source toolkit for statistical

machine translation,” in Proceedings of the 45th annual meeting of the

association for computational linguistics companion volume

proceedings of the demo and poster sessions, 2007, pp. 177–180.

G. Klein, Y. Kim, Y. Deng, J. Senellart, and A. M. Rush,

“Opennmt: Open-source toolkit for neural machine translation,”

Prepr. ArXiv170102810, 2017.

P. Koehn, F. J. Och, and D. Marcu, “Statistical phrase-based



P. F. Brown et al., “A statistical approach to machine

translation,” Comput. Linguist., vol. 16, no. 2, pp. 79–85, 1990.

P. F. Brown, S. A. Della Pietra, V. J. Della Pietra, and R. L.

Mercer, “The mathematics of statistical machine translation:

Parameter estimation,” Comput. Linguist., vol. 19, no. 2, pp. 263–311,

X. Li, G. Li, L. Liu, M. Meng, and S. Shi, “On the word

alignment from neural machine translation,” in Proceedings of the

th Annual Meeting of the Association for Computational

Linguistics, 2019, pp. 1293–1303.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R.

Salakhutdinov, “Dropout: a simple way to prevent neural networks

from overfitting,” J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929–1958,

L. M. Zintgraf, T. S. Cohen, T. Adel, and M. Welling,

“Visualizing deep neural network decisions: Prediction difference

analysis,” arXiv:1702.04595, 2017.

N. Kalchbrenner and P. Blunsom, “Recurrent continuous

translation models,” in Proceedings of the 2013 conference on

empirical methods in natural language processing, 2013, pp. 1700–

D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine

translation by jointly learning to align and translate,” ArXiv Prepr.

ArXiv14090473, 2014.

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical

evaluation of gated recurrent neural networks on sequence modeling,”

ArXiv Prepr. ArXiv14123555, 2014.

K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a

method for automatic evaluation of machine translation,” in

Proceedings of the 40th annual meeting of the Association for

Computational Linguistics, 2002, pp. 311–318.

Q. Gao and S. Vogel, “Parallel implementations of word

alignment tool,” in Software engineering, testing, and quality

assurance for natural language processing, 2008, pp. 49–57.

Virga, Paola and Khudanpur, Sanjeev. "Transliteration of

proper names in cross-lingual information retrieval" in

Transliteration of proper names in cross-lingual information

retrieval, 2003.



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