A neural-network based chat bot

In this paper, we explore the avenues of teaching computers to process natural language text by developing a chat bot. We take an experiential approach from a beginner level of understanding, in trying to appreciate the processes, techniques, the power and possibilities of natural language processing using recurrent neural networks (RNN). To achieve this, we kick started our experiment by implementing sequence to sequence long short-term memory cell neural network (LSTM) in conjunction with Google word2vec. Our results show the relationship between the number of training times and the quality of language model used for training our model bot affect the quality of its prediction output. Furthermore, they demonstrate reasoning and generative capabilities or RNN based chat bot.