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. Note that recordings of students' voices will not be retained to comply with privacy requirements – don't hesitate to talk in the lectures!

There is a shared Slack workspace for the course – email us to get access.

The schedule is the following:

Lectures + Labs: Tue 9:50-12:10
We start at 9:50 each week – the lab session is not taking place everytime, so sometimes we'll finish earlier.

Zoom meeting ID: 953 7826 3918
Password is the SIS code of this course (capitalized)

If you can't access Zoom, email us or text us on Slack.

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 from past lectures will appear here (video links are sent directly to students; email us if you want to get video links).

  1. Introduction (29 Sep)
  2. Machine Learning Toolkit (6 Oct)
  3. Data & Evaluation (13 Oct)
  4. Language Understanding (20 Oct)
  5. Dialogue State Tracking (27 Oct)


Lab assignments will appear in the dedicated GitLab repository.

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