Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation
Year: 2024
Type: article
Abstract: This research seeks to construct an automatic translation between Indonesian and Sundanese languages based on the Neural Machine Translation (NMT) method. The model used in this study is the Long Short-Term Memory (LSTM) type, which carries out an encoder-decoder structure model learned with Bible data. The text translation here was conducted in different epochs to optimize the process, followed by the Adam optimization algorithm. Testing the Adam optimizer with different epoch settings yields a BLEU score for Indonesian to Sundanese translations of 0.991785, higher than the performance of the None optimizer. Experimental results demonstrate that Indonesian to Sundanese translation using Adam optimization with 1000 epochs consistently performed better in BLEU - Bilingual Evaluation Understudy - scoring than Sundanese to Indonesian translation. Limitations of the research were also put forth, particularly technical issues related to the collection of data and the Sundanese language’s complex grammatical features, that the model can only partially express, honorifics, and the problem of polysemy. Also, it must be mentioned that no special hyperparameter selection was performed, as parameters were chosen randomly. In future studies, transformer-based models can be investigated since these architectures will better deal with complex language via their self-attention mechanism.
Authors: Anita Qotrun Nada, Aji Prasetya Wibawa, Dhea Fanny Putri Syarifa, Erliana Fajarwati, Fadia Irsania Putri
Institution: State University of Malang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $150
Funder: Universitas Negeri Malang
OpenAlex ID: W4406298230