Natural conversation is a hallmark of intelligent systems and thus dialog systems have been a key sub-area of Artificial Intelligence research for decades. Chatbots are their most recent incarnation and have been widely adopted, particularly in the recent COVID-19 pandemic, as sources of information. Given the increasing interest, there has been a surge in the development of easy-to-use platforms to rapidly create simple rule-based dialog agents. Given the rapid advances in natural language generation models, there is a great need to foster and guide the research on the development and deployment of dialog systems.

In addition to their applications in Companion Technology, chatbots have been increasingly used for providing assistance and advice during the COVID-19 pandemic. The aim is to implement technical systems that smartly adapt their functionality to their users’ individual needs and requirements and solve problems in close co-operation with users. They need to enter into a dialog and convincingly explain their suggestions and decision-making behavior.

Given their widespread adoption, there is a need to build dialog systems that can explain their reasoning and can stand up to ethical standards demanded in real-life settings. Despite the impressive applications of chatbots, the wider problem of dialog management is unaddressed, with mainstream tools supporting rudimentary rule-based processing. There is an urgent need to highlight the crucial role of reasoning methods, like constraints satisfaction, planning, and scheduling can play to build an end-to-end conversation system that evolves over time.

The workshop is the fourth edition of the Workshop on Reasoning and Learning for Human-Machine Dialogues. The past editions of the workshop were huge successes attracting 100+ AI researchers to discuss a variety of topics. DEEP-DIAL 21 will have reviewed paper presentations, invited talks, panels, and open contributions of datasets and chatbots. Accepted papers will be preferentially considered for Frontiers Journal's Open Access Special Issue on Collaborative Assistants for Society.