An intelligent Chatbot using deep learning with Bidirectional RNN and attention model

This paper shows the modeling and performance in deep learning computation for an Assistant Conversational Agent (Chatbot). The utilization of Tensorflow software library, particularly Neural Machine Translation (NMT) model. Acquiring knowledge for modeling is one of the most important task and quite difficult to preprocess it. The Bidirectional Recurrent Neural Networks (BRNN) containing attention layers is used, so that input sentence with large number of tokens (or sentences with more than 20–40 words) can be replied with more appropriate conversation. The dataset used in the paper for training of model is used from Reddit. The model is developed to perform English to English translation. The main purpose of this work is to increase the perplexity and learning rate of the model and find Bleu Score for translation in same language. The experiments are conducted using Tensorflow using python 3.6. The perplexity, leaning rate, Bleu score and Average time per 1000 steps are 56.10, 0.0001, 30.16 and 4.5 respectively. One epoch is completed at 23,000 steps. The paper also study MacBook Air as a system for neural network and deep learning.