SIS code: 

Statistical Dialogue Systems

This is the new version of the course for the '20/21 Fall semester. You can have a look at last year's version for old slides and more information.


This course presents 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

The course will be taught online over Zoom, given the current pandemic. All lectures will be recorded so you can catch up later.

The preliminary schedule (subject to change) is the following:

Lectures: Tue 10:40
Labs: Tue 9:50 (not every week, starting in the 2nd week)

All enrolled students will get a Zoom link via email. If you want to take part and haven't officialy enrolled

To successfully finish this course, you'll need to:

  • pass an exam (covering the lectures, especially parts mentioned in the summary) – the exam will either be written in person or oral over Zoom, depending on the situation
  • finish a small lab homework + a big lab project (individual or in groups) – implement some dialogue system experiments and write a report.


Slides and videos from past lectures will appear here (if all students agree, otherwise video links will be shared with students privately).


Lab assignmentswill appear in the dedicated GitLab repository (link coming soon).

Covered topics

  • Brief introduction into dialogue systems
    • dialogue systems applications
    • basic components of dialogue systems
    • knowledge representation in dialogue systems
    • data and evaluation
  • Language understanding (NLU)
    • semantic representation of utterances
    • statistical methods for NLU
  • 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
  • End-to-end dialogue systems (one network to handle everything)
    • sequence-to-sequence systems
    • memory/attention-based systems
    • pretrained language models
  • Open-domain systems (chatbots)
    • generative systems (sequence-to-sequence, hierarchical models)
    • information retrieval
    • ensemble systems
  • Multimodal systems
    • component-based and end-to-end systems
    • image classification
    • 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