Translation of Sentence Lampung-Indonesian Languages with Neural Machine Translation Attention Based Approach

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 %.