Optimasi Kombinasi Hyperparameter dan Augmentasi Korpus dalam Neural Machine Translation Bahasa Indonesia ke Bahasa Melayu Bengkulu
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Dewi Soyusiawaty
*
Universitas Ahmad Dahlan, Yogyakarta,
Indonesia
(*) Corresponding Author
Abstract
Neural Machine Translation (NMT) with attention mechanism has become an effective approach in improving the quality of cross-language translation. However, the application of NMT with attention to regional or minority languages still faces challenges, especially in the context of Bengkulu Malay Language, a variant of Malay Language used in the Bengkulu Province, Indonesia. This research aims to enhance the translation accuracy from Indonesian to Bengkulu Malay Language through optimization of hyperparameter combinations in NMT models with attention. The research method involves experiments with various hyperparameter combinations, such as batch size, dataset size, and dropout rate, applied to NMT models with attention. Evaluation is conducted using the BLEU metric to measure translation quality. Corpus augmentation is done to obtain a larger corpus.The experimental results indicate that translation accuracy improvement can be achieved by selecting optimal hyperparameter combinations. The use of a larger dataset yields better performance compared to a smaller dataset. A batch size of 16 yields better results than batch sizes of 32 and 64, especially when used with a larger dataset. Additionally, a dropout rate of 0.8 tends to perform better than dropout rates of 0.2 and 0.5. Regarding epoch values, the research shows that increasing epochs up to a certain point (approximately 30 epochs) enhances model performance, but further increases tend to cause overfitting on the training data. This research provides a significant contribution to the development of machine translation for Bengkulu Malay Language and other regional languages. It is hoped that the findings of this research can serve as a foundation for further development in the field of machine translation for minority languages, as well as improving information accessibility in diverse language communities in Indonesia.
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References
H. D. Ismadi, “Kebijakan Pelindungan Bahasa Daerah dalam Perubahan Kebudayaan Indonesia,” 2022.
Zaenal Abidin, Adi Sucipto, Arief Budiman, “PENERJEMAHAN KALIMAT BAHASA LAMPUNG-INDONESIA dengan NMT Attention,” 2018.
Yustiana Fauziyah, Ridwan Ilyas, Fatan Kasyidi, “Mesin Penterjemah Bahasa Indonesia - Bahasa Sunda menggunakan RNN,” Teknoinfo, 2022.
Lo Bun San, Herry Sujaini, Tursina, “Uji Nilai Akurasi pada NMT Bahasa Indonesia ke Bahasa Tiociu Pontianak dengan Mekanisme Attention Bahdanau,” JEPIN, 2023.
Fadel Razsiah, Ahmat Josi, Sari Mubaroh, “Aplikasi Penerjemah Bahasa Bangka Ke Bahasa Indonesia dengan NMT Berbasis Web,” Jurnal Inovasi Teknologi Terapan, 2023.
Nan Peng, Yue Wang, Yingying Wei, Lu Liu, Lei Wang, Yu Wang, “Quality Evaluation Model of Automatic Machine Translation based on Deep Learning Algorithm,” dalam International Conference for Emerging Technology (INCET), Belgaum, India, 2023.
Dzulkahfi, Herry Sujaini, Tursina, “Perbandingan Hasil Penerjemahan Neural Machine Translation dengan MarianNMT terhadap sumber korpus Wikimedia dan QED&TED,” JURISTI, 2023.
Yustiana Fauziyah, Ridwan Ilyas, Fatan Kasyidi, “MESIN PENTERJEMAH BAHASA INDONESIA-BAHASA SUNDA MENGGUNAKAN RECURRENT NEURAL NETWORKS,” TEKNOINFO, 2022.
Irmawati Carolina, Toto Haryanto, “Modeling Of Hyperparameter Tuned RNN-LSTM and Deep Learning For Garlic Price Forecasting In Indonesia,” 2024.
Naufal Ananda, Haryas Subyantara Wicaksana, Yusuf Giri, “HYPERPARAMETER TUNING LSTM SEBAGAI ESTIMATOR SENSOR RELATIVE HUMIDITY PADA AUTOMATIC WEATHER STATION BERBASIS SIMULATED ANNEALING,” 2023.
WILSON WONGSO , ANANTO JOYOADIKUSUMO , BRANDON SCOTT BUANA, DERWIN SUHARTONO, “Many-to-Many Multilingual Translation Model for Languages of Indonesia,” 2023.
Bert Le Bruyn, Martín Fuchs , Martijn van der Klis , Jianan Liu, Chou Mo, Jos Tellings, Henriëtte de Swart, “Parallel Corpus Research and Target Language Representativeness: The Contrastive, Typological, and Translation Mining Traditions,” 2022.
AJAY SHRESTHA AND AUSIF MAHMOOD, “Review of Deep Learning Algorithms and Architectures,” IEEE Access, 2019.
“Perancangan Mesin Translasi berbasis Neural dari Bahasa Kawi ke dalam Bahasa Indonesia menggunakan Microframework Flask,” JSI, 2022.
Teguh Ikhlas Ramadhan, Nur Ghaniaviyanto Ramadhan, Agus Supriatman, “Implementation of Neural Machine Translation for English-Sundanese Language using Long Short Term Memory (LSTM),” BITS, 2022.
Muhammad Yusuf Aristyanto, Robert Kurniawan, “Pengembangan Metode Neural Machine Translation Berdasarkan Hyperparameter Neural Network (Studi Kasus: Bahasa Jerman – Inggris),” 2021.
Jinyi Zhang and Tadahiro Matsumoto, “Corpus Augmentation for Neural Machine Translation with Chinese-Japanese Parallel Corpora,” MDPI, 2019.
Sree Harsha Ramesh and Krishna Prasad Sankaranarayanan, “Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora,” 2018.
Andry Chowandaa, Alan Darmasaputra Chowandab, “Generative Indonesian Conversation Model using Recurrent Neural Network with Attention Mechanism,” 2018.
Jiatao Gu, Hany Hassan, Jacob Devlin, Victor O.K. Li†, “Universal Neural Machine Translation for Extremely Low Resource Languages,” dalam Proceedings of NAACL-HLT, 2018.
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