SubjectsSubjects(version: 850)
Course, academic year 2019/2020
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Statistical Dialogue Systems - NPFL099
Title in English: Statistické dialogové systémy
Guaranteed by: Institute of Formal and Applied Linguistics (32-UFAL)
Faculty: Faculty of Mathematics and Physics
Actual: from 2019 to 2019
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:2/1 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Guarantor: Mgr. et Mgr. Ondřej Dušek, Ph.D.
Annotation -
Last update: Mgr. Barbora Vidová Hladká, Ph.D. (25.01.2019)
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.
Course completion requirements -
Last update: Mgr. Barbora Vidová Hladká, Ph.D. (13.05.2019)

Passing the final exam (written test based on the contents of lectures), finishing lab session homeworks (implementation of machine learning models for dialogue systems).

Literature -
Last update: Mgr. Barbora Vidová Hladká, Ph.D. (13.05.2019)
  • Jurafsky & Martin: Speech & Language processing. 3rd ed. draft, Chapter 24-25, https://web.stanford.edu/~jurafsky/slp3/

  • Lemon & Pietquin (eds.): Data-Driven Methods for Adaptive Spoken Dialogue Systems. Springer 2012. http://www.springer.com/gb/book/9781461448020

  • Rieser & Lemon: Reinforcement learning for adaptive dialogue systems. Springer 2011. http://www.springer.com/gp/book/9783642249419

  • Jokinen & McTear: Spoken dialogue systems. Morgan & Claypool 2010. https://books.google.co.uk/books/about/Spoken_Dialogue_Systems.html?id=ualwulnD020C

  • McTear et al.: The Conversational Interface: Talking to Smart Devices. Springer 2016.

  • Gao et al.: Neural Approaches to Conversational AI: Question Answering, Task-oriented Dialogues and Social Chatbots. now publishers 2019. http://arxiv.org/abs/1809.08267

  • Psutka et al.: Mluvíme s počítačem česky. Academia 2006.

  • + current papers from the field

Syllabus -
Last update: Mgr. Barbora Vidová Hladká, Ph.D. (25.01.2019)

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

 
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