Abstract
In this research, automatically Lampung language translation into the Indonesian language was using neural machine translation (NMT) attention based approach. NMT, a new approach method in machine translation technology, that has worked by combining the encoder and decoder. The encoder in NMT is a recurrent neural network component that encrypts the source language to several length-stable vectors and the decoder is a recurrent neural networks component that generates translation result comprehensive. NMT Research has begun with creating a pair of 3000 parallel sentences of Lampung language (api dialect) and Indonesian language. Then it continues to decide the NMT parameter model for the data training process. The next step is building NMT model and evaluate it. The testing of this approach has used 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 plural sentences without OOV, and 25 plural sentences with OOV. The testing translation result using NMT attention shows the bilingual evaluation understudy (BLEU) an average value is 51, 96 %.
Highlights
In this research, automatically Lampung language translation into the Indonesian language was using neural machine translation (NMT) attention based approach
that has worked by combining the encoder and decoder
the decoder is a recurrent neural networks component that generates translation result comprehensive. NMT Research has begun with creating a pair of 3000 parallel sentences of Lampung language
Summary
Eksperimen pertama, kedua dan ketiga dilakukan dengan menggunakan data latih 3000 kalimat, validasi eksperimen dilakukan dengan mekanisme 5-folds validation pada 3000 kalimat tersebut kemudian data uji yang digunakan 100 kalimat terdiri dari 25 kalimat tunggal tanpa out of vocobulary (OOV), 25 kalimat tunggal dengan OOV, 25 kalimat majemuk tanpa OOV dan 25 kalimat majemuk dengan OOV. Pengaturan file konfigurasi dilakukan sebelum masuk ke proses training data. Pengaturan file konfigurasi adalah penentuan parameter yang akan digunakan pada proses training data. Penetapan dilakukan dengan memberikan nilai-nilai yang berkesesuain dan disimpan dalam sebuah file bernama THUMT.config
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