The development of a chatbot using Convolutional Neural Networks

Abstract

Chatbots are artificial intelligence systems that comprehend the intent, context, and sentiment of the user, interact properly with them leading to an increased development of their creation, the past few years. In this study, Convolutional Neural Networks (CNNs) are applied as the classifier and some specific tools for tokenization are used for the creation of a chatbot. Taking into account that it is difficult to apply any algorithm in text, we use a technique called “Word Embedding”, which converts a text into numbers in order to run text processing. Specifically, “Word2Vec” Word Embedding technique was applied. AlexNet, LeNet5, ResNet and VGGNet CNN architectures were utilised. These architectures were compared for their accuracy, f1 score, training time and execution time. The results obtained highlighted that there were significant differences in the performance of the architectures applied. The most preferable architecture of our study was LeNet-5 having the best accuracy compared to other architectures, the fastest training time, and the least losses whereas it was not very accurate on smaller datasets such as our Test Set. Limitations and directions for future research are also presented.