Recurrent Neural Network to Deep Learn Conversation in Indonesian

Natural Language Processing (NLP) is still considered a daunting task to solve for us, researcher in this field. Specifically, there is not many research has been done in a local language like Indonesian Language. Nowdays, there are hundreds of systems that require NLP as their main functions. This could be a good opportunity for us to explore this opportunity. This paper contributes models from deep learning training in Indonesian conversation using dual encoder LSTM as well as vector representation models trained with three corpora using Skip-gram method. The results show that the models are able to make a good correlation, synonym from a particular word in the words representation of vector models. In addition, the conversation models resulted in 1.07 of perplexity in the Combined model in the 14000th steps.