ARCHIVES

Abstract: Although there are many ways to build chatbots, they lack the human touch and sound very robotic. We don't have chatbots designed to mimic personality or human-like characteristics, although we have everything we need in terms of data and computing. The goal of this project is to make an efficient as well as human-like chat with a modern encoder-decoder. There are many frameworks and libraries available for developing AI-based chatbots, including program-based, rule-based, and interface-based. But they lack the flexibility to develop a real dialogue and understand people. The use of chatbots has grown rapidly in recent years due to their ability to handle repetitive tasks, provide 24/7 customer support and improve customer engagement. Developing chatbots requires the use of natural language processing (NLP) technology to understand and generate human responses. In this project, we propose a behavior-based chat that uses encoder-decoder architecture. An encoder-decoder architecture is a neural network that can receive input sequences and output the corresponding network that can receive input sequences and output the corresponding sequence. The encoder component converts the input sequence into a fixed-length vector, while the decoder component creates an output sequence based on the encoded vector.Our proposed chatbot model uses a sequence-to-sequence (seq2seq) architecture with an attention mechanism to improve accuracy. of the responses generated. The chatbot model is trained on a dataset of customer support conversations to learn human conversational patterns and behavior.