Building Intelligent Chatbots: Tools, Technologies, and Approaches

This paper aims to provide a comprehensive overview of the approaches and technologies used in building chatbots, including declarative and open-domain chatbots, and to serve as a useful resource for researchers and practitioners working in this field. We review pipeline methods, which involve the use of various techniques to process, understand, and generate natural language utterances, and end-to-end methods, which involve training a single neural network to handle all aspects of the conversation. We also examine neural generative models, which use machine learning techniques to generate responses based on input data, and retrieval-based methods, which use pre-defined responses and match them to user input. Furthermore, we review the various technologies used in building chatbots, including natural language processing (NLP) libraries, frameworks, non-cloud-based platforms, and cloud-based platforms. We provide a comparison of the strengths and limitations of these approaches and technologies and discuss the potential future directions for research in this field.