Architectural Insights: A Journey in Chatbot Development

Abstract

This thesis delves into a detailed examination of the effectiveness of various neural architectures for the development of chatbots. The primary focus is on exploring the capabilities of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Transformers. Through an in-depth review and comparative analysis, the research endeavors to shed light on the specific strengths, weaknesses, and practical implications of each architecture within the domain of chatbot development. Furthermore, the study delves into the process of implementing chatbots using these architectural models. In addition, the thesis provides a comprehensive case study that leverages Dialogflow, a widely utilized conversational AI platform, to showcase the practical application of these diverse neural architectures in real-world chatbot development scenarios.