Evolutionary Approaches Toward Traditional to Deep Learning-Based Chatbot

What is it about?

As the world is getting modern day by day, the use of artificial intelligence is also increasing exponentially. The uses of smart websites and applications are also booming. Hence, the demand of some smart assistants like chatbots is higher than ever. The concept behind chatbot includes the use of both natural language processing and ML/neural network. In this work of developing a chatbot, we first attempted to build a model, where the input sentences will be converted in a form, which can be easily fed to a ML/neural network algorithm. After this was complete, several ML algorithms were tried and tested to work behind the model, and a comparative study was done between them. Not only simple ML models, some neural network concepts like artificial neural network and feed forward neural network were also applied and discussed to determine which algorithm gave the best result with the chatbot and the reason behind it. In the end, we added a UI above everything, to make it more attractive. The details of each and every aspect are given in the paper.

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Why is it important?

This paper addresses the growing demand for smart assistants like chatbots by developing a comprehensive model integrating natural language processing and ML/neural network algorithms, followed by a comparative analysis of various ML and neural network approaches to optimize chatbot performance, culminating in an enhanced user interface for improved interaction.

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