About this Research Topic
Conversational AI (also referred to as digital conversational agents or chatbots) enables humans to converse in written, spoken, or multimodal natural language with machines. Supported by developments in deep learning-based Natural Language Processing as well as insights from linguistics, cognition, and psychology, the field is gearing up to leave behind classical, script-based conversational agents, and increasingly makes the connection with neural memory, attention, and relevance feedback learning.
To support research in this area, we launched a special Research Topic and invite academic papers on the following topics:
- Dialogue intent detection
- Conversational memory structure
- Neural attention (e.g. Transformers) for conversational memory management (storage, retrieval, updates)
- Reinforcement learning for adaptive dialogue management
- Multi-modal conversational AI (possibly including deixis, visual communication, or other communication modes, like voice)
- Effective human-machine teaming using conversational AI
- Emergent communicative behavior in machines through interaction with humans
- Resources (data collection) for training AI-based conversational AI, such as dialogue corpora
- Methodologies and experimental designs for evaluating conversational AI
- Applications of conversational AI, demonstrating the added value
- Affect and conversational AI
- Long-term engaging interactions with conversational AI
- Repair mechanisms in conversational AI
- Natural Language Generation for conversational AI
- Explainable, transparent conversational AI
- Pragmatics and speech acts for human-machine dialogue
- Knowledge graph-driven dialogue management
- Belief and knowledge revision in human-machine dialogues
- (Technological solutions to) ethical issues in conversational AI
We cordially invite you to submit a 2-page abstract describing your research. Submissions will be judged on relevance, impact, novelty, clarity, and technical and scientific quality. Concrete results will be preferred over opinion papers.
To support research in this area, we launched a special Research Topic and invite academic papers on the following topics:
- Dialogue intent detection
- Conversational memory structure
- Neural attention (e.g. Transformers) for conversational memory management (storage, retrieval, updates)
- Reinforcement learning for adaptive dialogue management
- Multi-modal conversational AI (possibly including deixis, visual communication, or other communication modes, like voice)
- Effective human-machine teaming using conversational AI
- Emergent communicative behavior in machines through interaction with humans
- Resources (data collection) for training AI-based conversational AI, such as dialogue corpora
- Methodologies and experimental designs for evaluating conversational AI
- Applications of conversational AI, demonstrating the added value
- Affect and conversational AI
- Long-term engaging interactions with conversational AI
- Repair mechanisms in conversational AI
- Natural Language Generation for conversational AI
- Explainable, transparent conversational AI
- Pragmatics and speech acts for human-machine dialogue
- Knowledge graph-driven dialogue management
- Belief and knowledge revision in human-machine dialogues
- (Technological solutions to) ethical issues in conversational AI
We cordially invite you to submit a 2-page abstract describing your research. Submissions will be judged on relevance, impact, novelty, clarity, and technical and scientific quality. Concrete results will be preferred over opinion papers.
Keywords: Next generation conversational agents, memory based deep learning, semantically aware AI systems
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.
