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A Deep Neural Network-Based Yoruba Intelligent Chatbot System
(iSTEAMS, 2022-06-14) Babatunde, A.N., Oke, A.A., Balogun, B.F., AbdulRahman, T.A. & Ogundokun, R.O.
Two Artificial Intelligence software systems, Bot and Chatbot have recently debuted on the internet. This initiate a communication between the user and a virtual agent. The modeling and performance in deep learning (DL) computation for an Assistant Conversational Agent are presented in this research (Chatbot). The deep neural network (DNN) technique is used to respond to a large number of tokens in an input sentence with more appropriate dialogue. The model was created to do Yoruba-to-Yoruba translations. The major goal of this project is to improve the model's perplexity and learning rate, as well as to find a blue score for translation in the same language. Kares is used to run the experiments, which is written in Python. The data was trained using a deep learning-based algorithm. With the use of training examples, a collection of Yoruba phrases with various intentions was produced. The results demonstrate that the system can communicate in basic Yoruba terms and that it would be able to learn simple Yoruba words. The study result when evaluated showed that the system had 80% accuracy rate. Keywords: Chatbot, Natural Language Processing, Deep Learning, Artificial Neural Network, Yoruba Language