Deep neural networks have revolutionized many fields, including Natural Language Processing. This paper outlines teaching materials for an introductory lecture on deep learning in Natural Language Processing (NLP). The main submitted material covers a summer school lecture on encoder-decoder models. Complementary to this is a set of jupyter notebook slides from earlier teaching, on which parts of the lecture were based on. The main goal of this teaching material is to provide an overview of neural network approaches to natural language processing, while linking modern concepts back to the roots showing traditional essential counterparts. The lecture de- parts from count-based statistical methods and spans up to gated recurrent networks and attention, which is ubiquitous in today’s NLP.
|Title of host publication
|TeachNLP workshop at NAACL 2021
|Association for Computational Linguistics
|Published - Jun 2021