Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots

TLDR

Conversational AI has evolved from early voice/text interaction concepts to modern digital assistants, smart speakers, and chatbots, driven by deep learning, abundant computing power, and large datasets, with current research emphasizing machine‑learning and statistical data‑driven dialogue systems while acknowledging legacy technologies and outlining future challenges such as multimodality, knowledge‑graph integration, and ethical issues. This book offers a comprehensive introduction to Conversational AI. It reviews three primary dialogue‑system approaches—rule‑based handcrafted systems, statistical data‑driven machine‑learning models, and neural end‑to‑end learning—and discusses evaluation metrics and frameworks for assessing performance and usability.

Abstract

This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.