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Imitation and Reinforcement Learning for NLP

Module Description

Course	Module Abbreviation	Credit Points
BA-2010	AS-CL	8 LP
Master	SS-CL, SS-TAC	8 LP

Lecturer	Artem Sokolov
Module Type	Projektseminar
Language	Englisch
First Session	13.11.2020
Time and Place	Friday, 09:15-10:45, Online
Commitment-Frist	tbd.

Prerequisite for Participation

Knowledge of the following will be helpful: foundations of statistical machine Learning, reinforcement learning and neural networks.

Assessment

Implementation project

Content

This module is an software implementation project of imitation learning approaches to sequence-to-sequence learning problems with a focus on neural machine translation. Closely related to structured prediction and reinforcement learning, imitation learning is particularly suited for sequence prediction tasks, where good success metrics or intermediate rewards are hard to define, while in the same time it is easy to provide demonstrations of correct behavior.

Literature

Sutton and Barto "Reinforcement Learning" (2018) 2nd edition. MIT Press. http://incompleteideas.net/book/the-book-2nd.html

Szepesvari (2010). Algorithms for Reinforcement Learning. Morgan & Claypool. https://sites.ualberta.ca/~szepesva/RLBook.html

Osa, Pajarinen, Neumann, Bagnell, Abbeel, Peters (2018), An Algorithmic Perspective on Imitation Learning. Foundations and Trends in Robotics. https://arxiv.org/abs/1811.06711

Daumé III, A Course in Machine Learning, Chapter 18 http://ciml.info/

Goldberg (2015). A Primer on Neural Network Models for Natural Language Processing. https://arxiv.org/abs/1510.00726