About this Research Topic
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 dialog agents at different levels of sophistication. Further, with the rapid advances in natural language generation models, there is a need to foster and guide the research on the development and deployment of dialog systems with what users actually value.
The COVID-19 pandemic has been an opportunity to validate the relevance of collaborative assistance technologies for real-world needs. Chatbots have been increasingly used for seeking advice and providing assistance related to symptoms, health facilities and public policies. The usage aim is to implement technical systems that smartly adapt their functionality to their users’ individual needs and requirements and solve problems in close cooperation with users. They need to enter into a dialog and convincingly explain their suggestions and decision-making behavior.
Such applications highlight future research directions for the community. 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. The impressive gains of learning-based models to discover insights from data have to be married with pre-known knowledge - e.g., common-sense and spatio-temporal knowledge - to be usable by the common man. 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 systems have to be deployable at lower cost and usable in situations with limited device capabilities and network connectivity.
Collaborative Assistants (CA) is a general term we use for conversation systems, chatbots, dialog systems and digital assistants. They have been adopted for mainstream applications in several industries, with many frameworks available from companies, in addition to universities, to build and deploy them at commercial scale. However, beyond basic demonstration, there is little experience in how they can be designed and used for real-world applications needing decision making under constraints (e.g., sequential decision making). The Research Topic will thus be timely to help Collaborative Assistants realize their full potential. Both the technical aspects of how efficiently CAs could be built, and the practical aspects of how they can be effectively used in applications, will be of interest to the special issue.
The proposers of this special issue have conducted DEEP-DIAL workshops at AAAI (2018-2020; scheduled for 2021) to enable the discussion by bringing the learning and reasoning sub-communities together. The participants of the DEEP-DIAL workshops will have the opportunity of submitting extended versions of their research papers to this Research Topic. Further, the lead proposer is also planning a virtual conference on Collaborative Assistants in October 2020, and the presenters will be invited to contribute.
Topics of interested are, non-exclusive:
1. Dialog Systems
- Design considerations for dialog systems
- Evaluation of dialog systems, metrics
- Open-domain dialog and chat systems
- Task-oriented dialog
- Style, voice, and personality in spoken dialog and written text
- Novel Methods for NL Generation for dialogs
- Early experiences with implemented dialog systems
- Mixed-initiative dialog where a partner is a combination of agent and human
- Hybrid methods
- Applications in under-served domains: Health, Education, Governance
- Domain model acquisition, especially from unstructured text
- Plan recognition in natural conversation
- Planning and reasoning in the context of dialog systems
- Handling uncertainty
- Optimal dialog strategies
- Learning to reason
- Learning for dialog management
- End2end models for conversation
- Explaining dialog policy
4. Practical Considerations
- Responsible chatting
- Ethical issues with learning and reasoning in dialog systems
- Corpora, Tools, and Methodology for Dialog Systems
- Securing one’s chat
- Multi-modal chatbots
- Auditing Chatbots
Keywords: Conversation Agents, Digital Assistants, Human-Computer Collaboration, Learning, Reasoning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.