This is an archived version of the course as taught in the '19/20 Fall semester. To see the current academic year, go here.

Statistical Dialogue Systems


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 follow-up to the course NPFL123 Dialogue Systems, but can be taken independently – important basics will be repeated. All required deep learning concepts will be explained, but only briefly, so some machine learning background is recommended.


The course will be taught in English, but we're happy to explain in Czech as well.statistical dialogue system schema

Lectures: Thu 10:40 S1 (room changed!)
Labs: Thu 14:00 SW1
(labs will primarily take place online & in groups, with a few meetings during the semester)

To successfully finish this course, you'll need to pass a written exam (covering the lectures, especially parts mentioned in the summary) and participate in a lab project. More info on the exam is at the end of last lecture's slides.


Slides from past lectures will appear here:

The last lecture slides include information about the exam.


Lab assignments appear in the dedicated GitLab repository.

Topics to be covered

  • 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

Recommended reading

  • McTear et al.: The Conversational Interface: Talking to Smart Devices. Springer 2016.
  • Psutka et al.: Mluvíme s počítačem česky. Academia 2006.
  • + current papers from the field