The Influence of Word Vectorization for Kawi Language to Indonesian Language Neural Machine Translation

Abstract

People relatively use machine translation to learn any textual knowledge beyond their native language. There is already robust machine translation such as Google translate. However, the language list has only covered the high resource language such as English, France, etc., but not for Kawi Language as one of the local languages used in Bali's old works of literature. Therefore, it is necessary to study the development of machine translation from the Kawi language to the more active user language such as the Indonesian language to make easier learning access for the young learner. The research developed the neural machine translation (NMT) using recurrent neural network (RNN) based neural models and analyzed the influence of word vectorization using Word2Vec for the machine translation performance based on BLEU scores. The result shows that word vectorization indeed significantly increases the NMT models performance, and Long-Short Term Memory (LSTM) with attention mechanism has the highest BLEU scores equal to 20.86. The NMT models still could not achieve the BLEU scores on par with those human experts and high resource language machine translation. On the other hand, this initial study could be the reference for the future development of Kawi to Indonesian NMT.