| 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. | ||