Comparative Analysis of Deep Learning Techniques for Chatbots Implementation

Nowadays, advancements in technology have facilitated the development of machines that emulate human-like behavior. One notable application of this is conversational software agents which utilize natural language process (NLP) and artificial intelligence (AI), commonly referred to as Chatbots. These Chatbots serve various purposes and find application across diverse sectors such as business, healthcare, agriculture, and more. The evolution of Chatbots has been remarkable, prompting significant interest and research endeavours. This paper aims to provide a comprehensive survey of the techniques employed in enhancing Chatbot functionalities, offering a comparative analysis among them. Additionally, it delves into the nuances of these techniques, elucidating both their commonalities and distinctions. Through this exploration, the most commonly used algorithms in chatbots are reinforcement learning, BERT, NLP, and hybrid algorithms of artificial neural networks. The paper endeavours to identify research gaps that can guide future improvements in Chatbot technology, ultimately enhancing customer satisfaction levels.