Natural conversation is a hallmark of intelligent systems. Unsurprisingly, dialog systems have been a key sub-area of AI for decades. Their most recent form, chatbots, which can engage people in natural conversation and are easy to build in software, have been in the news a lot lately. There are many platforms to create dialogs quickly for any domain, based on simple rules. Further, there is a mad rush by companies to release chatbots to show their AI capabilities and gain market valuation. However, beyond basic demonstration, there is little experience in how they can be designed and used for real-world applications that need decision making under constraints (e.g., sequential decision making). The workshop will thus be timely in helping chatbots realize their full potential.
Furthermore, there is an upcoming interest and need for innovation in Human-Technology-Interaction, as addressed in the context of Companion Technology. Here, the aim is to implement technical systems that smartly adapt their functionality to their users’ individual needs and requirements and are even able to solve problems in close co-operation with human users. To this end, they need to enter into a dialog and convincingly explain their suggestions and decision-making behavior.
From research side, statistical and machine learning methods are well entrenched for language understanding and entity detection. However, 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 such as constraints satisfaction, planning and scheduling, and learning working together with them, that can play to build an end-to-end conversation system that evolves over time. From the practical side, conversation systems need to be designed for working with people in a manner that they can explain their reasoning, convince humans about making choices among alternatives, and stand up to ethical standards demanded in real life settings.
Thus, recognizing the need for more research attention, the proposers of the current workshop organized the highly successful DEEP-DIAL18 workshop at AAAI 2018 (Photos). The event brought together over 100 AI researchers from around the world to discuss a bouquet of research topics around human-machine dialogs. The program included 4 invited talks, 7 reviewed full paper presentations and 4 lightening talks accompanied by posters, and a topical panel discussion. Buoyed by the success, we ran the second edition, DEEP-DIAL19, at AAAI 2019. It was again a huge success attracting 100+ AI researchers to discuss a variety of topics. The program included 3 invited talks, 7 review full paper presentations and 5 lightening talks accompanied by posters followed by a highly engaging panel discussion.
For DEEP-DIAL 2020, topics of Interest Include:
Dialog Systems
Reasoning
Learning
Practical Considerations
The intended audience students, academic researchers and practitioners with an industrial background from the AI sub-areas of dialog systems, learning, reasoning, planning, HCI, ethics and knowledge representation.