Syllabus
Brief introduction into dialogue systems
- dialogue systems applications
- basic components of dialogue systems
- knowledge representation in dialogue systems
- data and evaluation
Language understanding (SLU)
- semantic representation of utterances
- statistical methods for SLU
Dialogue management
- dialogue representation as a (Partially Observable) Markov Decision Process
- dialogue state tracking
- action selection
- reinforcement learning
- user simulation
- deep reinforcement learning (using neural networks)
Response generation (NLG)
- introduction to NLG, basic methods (templates)
- generation using neural networks
Open-domain systems (chatbots)
- generative systems (sequence-to-sequence, hierarchical models)
- information retrieval
- ensemble systems
End-to-end dialogue systems
- training based on dialogue logs in a limited domain
- multi-task learning
Multi-domain systems
- one-shot learning
Multimodal systems
- visual dialogue
Annotation
This course will present advanced problems and current state-of-the-art in the field of dialogue systems, voice assistants, and conversational systems (chatbots). After a brief introduction into the topic, the course will focus mainly on the application of machine learning – especially deep learning/neural networks – in the individual components of the traditional dialogue system architecture as well as in end-to-end approaches (joining multiple components together). This course is a loose follow up to the course NPFL123
Dialogue Systems, but can be taken independently.