Intelligent Chatbot Adapted from Question and Answer System Using RNN-LSTM Model

Abstract

Abstract In modern times, the chatbot is implemented to store data collected through a question and answer system, which can be applied in the Python program. The data to be used in this program is the Cornell Movie Dialog Corpus which is a dataset containing a corpus which contains a large collection of metadata-rich fictional conversations extracted from film scripts. The application of chatbot in the Python program can use various models, the one specifically used in this program is the LSTM. The output results from the chatbot program with the application of the LSTM model are in the form of accuracy, as well as a data set that matches the information that the user enters in the chatbot dialog box input. The choice of models that can be applied is based on data that can affect program performance, with the aim of the program which can determine the high or low level of accuracy that will be generated from the results obtained through a program, which can be a major factor in determining the selected model. Based on the application of the LSTM model into the chatbot, it can be concluded that with all program test results consisting of a variety of different parameter pairs, it is stated that Parameter Pair 1 (size_layer 512, num_layers 2, embedded_size 256, learning_rate 0.001, batch_size 32, epoch 20) from File 3 is the LSTM Chatbot with the avg accuracy value of 0.994869 which uses the LSTM model is the best parameter pair.