Date | Topic |
Section I: Introduction / background |
Lecture 1 (Jan 19)
|
Introduction to NLP
|
Lecture 2 (Jan 22)
|
Basics of Deep Learning: Backpropagation and Neural Networks
|
Section II: NLP core techniques |
Lecture 3
|
Language Modeling and Recurrent Neural Networks
|
Lecture 4
|
Word Meaning and Word Embedding
|
Lecture 5
|
Sentence Embeddings, Convolutional Neural Networks
|
Lecture 6 & 7
|
Graph Representations for NLP, Graph Convolutional Network
|
Lecture 8
|
Machine Translation, Seq2Seq and Attention
|
Lecture 9
|
Transformer and BERT
|
Lecture 10
|
Pre-trained Language Models (student mini lectures)
|
Lecture 11
|
Constituency Parsing
|
Lecture 12
|
Syntactic Dependency Parsing
|
Section III: Cutting-edge research topics. |
Lecture 13
|
Data, Knowledge, and Logic: Modeling and Reasoning for Natural Language Understanding
|
Lecture 14
|
Unsupervised Text Generation (Guest lecture: Lili Mou, University of Alberta)
|
Lecture 15
|
Knowledge Graph (Guest lecture: Yanghua Xiao, Fudan University)
|
Lecture 16 & 17
|
Open-domain Question Answering (Conference tutorial: ACL 2020)
|
Lecture 18 & 19
|
Neural Approaches to Conversational AI (Conference tutorial: ICML 2019)
|
Lecture 20 & 21
|
Commonsense Knowledge Representation and Reasoning in Natural Language Processing (Conference tutorial: ACL 2020) |
Lecture 22 & 23
|
Course project presentations and discussions.
|