.png)
LSTM-Based Machine Translation for Madurese-Indonesian
Abstract
Madurese is one of the regional languages in Indonesia, which dominates East Java and Madura Island in particular. The use of Madurese as a daily language has declined significantly due to a language shift in children and adolescents, some of which are caused by a sense of prestige and difficulty in learning Madurese. The scarcity of research or scientific titles that raises the Madurese language also helps reduce literacy in the language. Our research focuses on creating a translation machine for Madurese to Indonesian to maintain and preserve the existence of the Madurese language so that learning can be done through digital media. This study use the latest dataset for the Madurese-Indonesian language by using a corpus of 30,000 Madura-Indonesian sentence pairs from the online Bible. This study scrapped online Bible pages to organize the corpus based on the Indonesian and Madurese bilingual Bible. Then This study manually process text to match the two languages' scrapping results, normalization, and tokenization to remove non-printable characters and punctuation from the corpus. To perform neural machine translation (NMT), This study connected the RNN encoder with the RNN decoder of the language model, while for training and testing, This study used a sequential model with LSTM, while the BLEU measure was used to assess the accuracy of the translation results. This study used the SoftMax optimization function with Adam Optimizer and added some settings, including using 128 layers in the training process and adding a Dropout layer so that This study got the average evaluation result for BLEU-1 is 0.798068, BLEU-2 is 0.680932, BLEU-3 is 0.623489, and for BLEU-4 is 0.523546 from five tests conducted. Given the language differences between Madurese and Indonesian, this can be the best approach for machine translation of Indonesian to Madurese.
Keywords
Full Text:
PDFReferences
A. F. Aji et al., ‘One Country, 700+ languages: NLP challenges for underrepresented languages and dialects in Indonesia’, ArXiv Prepr. ArXiv220313357, 2022.
A. Hermanto, T. B. Adji, and N. A. Setiawan, ‘Recurrent neural network language model for English-Indonesian Machine Translation: Experimental study’, in 2015 International conference on science in information technology (ICSITech), IEEE, 2015, pp. 132–136.
Z. Abidin, A. Sucipto, and A. Budiman, ‘Penerjemahan Kalimat Bahasa Lampung-Indonesia Dengan Pendekatan Neural Machine Translation Berbasis Attention Translation of Sentence Lampung-Indonesian Languages With Neural Machine Translation Attention Based’, J Kelitbangan, vol. 6, no. 02, pp. 191–206, 2018.
Q. A. Agigi, ‘Language Statistical Machine Translation Muna to Indonesia Language’, JATISI J. Tek. Inform. Dan Sist. Inf., vol. 8, no. 4, pp. 2173–2186, 2021.
A. E. P. Lesatari, A. Ardiyanti, and I. Asror, ‘Phrase Based Statistical Machine Translation Javanese-Indonesian’, J. MEDIA Inform. BUDIDARMA, vol. 5, no. 2, pp. 378–386, 2021.
A. P. Wibawa, A. Nafalski, and W. F. Mahmudy, ‘Javanese speech levels machine translation: improved parallel text alignment based on impossible pair limitation’, in 2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM), IEEE, 2013, pp. 16–20.
M. S. Alam and A. A. Suryani, ‘Minang and Indonesian Pharase-Based Statistical Machine Translation’, J. Inform. ℡ECOMMUNICATION Eng., vol. 5, no. 1, pp. 216–224, 2021.
I. G. B. A. Budaya, M. W. A. Kesiman, and I. M. G. Sunarya, ‘The Influence of Word Vectorization for Kawi Language to Indonesian Language Neural Machine Translation’, J. Inf. Technol. Comput. Sci., vol. 7, no. 1, pp. 81–93, 2022.
F. H. Rachman, N. Ifada, S. Wahyuni, G. D. Ramadani, and A. Pawitra, ‘ModifiedECS (mECS) Algorithm for Madurese-Indonesian Rule-Based Machine Translation’, in 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA), IEEE, 2022, pp. 51–56.
M. Yang, R. Wang, K. Chen, X. Wang, T. Zhao, and M. Zhang, ‘A Novel Sentence-Level Agreement Architecture for Neural Machine Translation’, IEEEACM Trans. Audio Speech Lang. Process., vol. 28, pp. 2585–2597, 2020, doi: 10.1109/TASLP.2020.3021347.
V. Macketanz, E. Avramidis, A. Burchardt, J. Helcl, and A. Srivastava, ‘Machine Translation: Phrase-Based, Rule-Based and Neural Approaches with Linguistic Evaluation’, Cybern. Inf. Technol., vol. 17, no. 2, pp. 28–43, Jun. 2017, doi: 10.1515/cait-2017-0014.
D. Puspitaningrum, ‘A Study of English-Indonesian Neural Machine Translation with Attention (Seq2Seq, ConvSeq2Seq, RNN, and MHA) A Comparative Study of NMT on English-Indonesian’, in 6th International Conference on Sustainable Information Engineering and Technology 2021, 2021, pp. 271–280.
I. Sutskever, O. Vinyals, and Q. V. Le, ‘Sequence to Sequence Learning with Neural Networks’. arXiv, Dec. 14, 2014. doi: 10.48550/arXiv.1409.3215.
G. Van Houdt, C. Mosquera, and G. Nápoles, ‘A review on the long short-term memory model’, Artif. Intell. Rev., vol. 53, pp. 5929–5955, 2020.
F. Stahlberg, ‘Neural Machine Translation: A Review’, J. Artif. Intell. Res., vol. 69, pp. 343–418, Oct. 2020, doi: 10.1613/jair.1.12007.
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.
Y. Fan, F. Tian, Y. Xia, T. Qin, X.-Y. Li, and T.-Y. Liu, ‘Searching Better Architectures for Neural Machine Translation’, IEEEACM Trans. Audio Speech Lang. Process., vol. 28, pp. 1574–1585, 2020, doi: 10.1109/TASLP.2020.2995270.
A. Pranolo, Y. Mao, A. P. Wibawa, A. B. P. Utama, and F. A. Dwiyanto, ‘Robust LSTM With tuned-PSO and bifold-attention mechanism for analyzing multivariate time-series’, IEEE Access, vol. 10, pp. 78423–78434, 2022.
A. P. Wibawa, I. T. Saputra, A. B. P. Utama, W. Lestari, and Z. N. Izdihar, ‘Long Short-Term Memory to Predict Unique Visitors of an Electronic Journal’, in 2020 6th International Conference on Science in Information Technology (ICSITech), IEEE, 2020, pp. 176–179.
K. Dedes, A. B. P. Utama, A. P. Wibawa, A. N. Afandi, A. N. Handayani, and L. Hernandez, ‘Neural Machine Translation of Spanish-English Food Recipes Using LSTM’, JOIV Int. J. Inform. Vis., vol. 6, no. 2, pp. 290–297, 2022.
S. Edunov, A. Baevski, and M. Auli, ‘Pre-trained language model representations for language generation’, ArXiv Prepr. ArXiv190309722, 2019.
K. B. Prakash, Y. V. R. Nagapawan, N. L. Kalyani, and V. P. Kumar, ‘Chatterbot implementation using transfer learning and LSTM encoder-decoder architecture’, Int. J., vol. 8, no. 5, 2020.
D. E. Cahyani, A. P. Wibawa, D. D. Prasetya, L. Gumilar, F. Akhbar, and E. R. Triyulinar, ‘Text-Based Emotion Detection using CNN-BiLSTM’, in 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), IEEE, 2022, pp. 1–5.
D. P. Kingma and J. Ba, ‘Adam: A method for stochastic optimization’, ArXiv Prepr. ArXiv14126980, 2014.
R. Bawden, B. Zhang, L. Yankovskaya, A. Tättar, and M. Post, ‘A study in improving BLEU reference coverage with diverse automatic paraphrasing’, ArXiv Prepr. ArXiv200414989, 2020.
P. Zaremoodi and G. Haffari, ‘Neural machine translation for bilingually scarce scenarios: a deep multi-task learning approach’, ArXiv Prepr. ArXiv180504237, 2018.
K. Kim, ‘New canonical decomposition and composition processes for Hangeul’, Comput. Stand. Interfaces, vol. 24, no. 1, pp. 69–82, 2002.
A. Rai and S. Borah, ‘Study of various methods for tokenization’, in Applications of Internet of Things: Proceedings of ICCCIOT 2020, Springer, 2021, pp. 193–200.
G. V. A. Gutiérrez, ‘A Comparative Study of NLP and Machine Learning Techniques for Sentiment Analysis and Topic Modeling on Amazon’, Int J Comput Sci Eng, vol. 9, no. 2, pp. 159–170, 2020.
M. IŞIK and H. Dağ, ‘The impact of text preprocessing on the prediction of review ratings’, Turk. J. Electr. Eng. Comput. Sci., vol. 28, no. 3, pp. 1405–1421, 2020.
DOI: https://doi.org/10.47738/jads.v4i3.113
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0