Instructor Lingpeng Kong (lpk AT
Season Fall 2022
Location CPD-LG.09
TA Yiheng Xu (yihengxu AT
Course description:

Natural language processing (NLP) is the study of human language from a computational perspective. The course will be focusing on machine learning and corpus-based methods and algorithms. We will cover syntactic, semantic and discourse processing models. We will describe the use of these methods and models in applications including syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. This course starts with language models (LMs), which are both front and center in natural language processing (NLP), and then introduces key machine learning (ML) ideas that students should grasp (e.g. feature-based models, log-linear models and then the neural models). We will land on modern generic meaning representation methods (e.g. BERT/GPT-3) and the idea of pretraining / finetuning.


COMP3314 or COMP3340, MATH1853


50% continuous assessment, 50% course project


Lecture     Topic/papers Recommended reading Materials
Sept. 5 Introduction to NLP, Language Models, RNNLM [slides] [J&M Ch. 1] [Lee, 2004] [J&M Ch. 4] [J&M Ch. 7] [M. Collins, Notes 1] [C. Dyer, LSTM Notes] [ Assignment1 ]
Sept. 14 Computational Graphs [slides] [J&M Ch. 8.1 - 8.3] [M. Collins, Notes]
Sept. 26 Sequence to Sequence Model and Attention [slides] [Sutskever et al, 2014][Baahdanau et al, 2015]
Oct. 3 Transformers[slides] [Vaswani et al, 2017] [The Annotated Transformer]
Oct. 17 BERT -- Pretraining + Finetuning & Parsing, Context-free Grammars [slides] [BERT paper] [M. Collins, Notes] [J&M Ch. 12] [ Final Project ]
[ Project Details ]
Oct. 24 Probabilistic Context-free Grammars, Recursive Neural Networks, Shift-reduce Parsing [slides] [Stanford Sentiment Treebank] [Socher et al, 2013] [Dyer et al, 2015]
Oct. 31 Recurrent Neural Network Grammars [slides] [Dyer et al., 2016] [Kuncoro et al., 2017] [ Assignment2 ]
Nov. 7 Dependency Parsing, Pretrained Language Models [slides] [J&M Ch. 14] [BART] [T5] [InfoWord] [GPT-3] [ELMo]
Nov. 14 Natural Language Generation, NLP and Vision [slides] [Holtzman et al., 2019] [Xu et al., 2015] [Assignment3]
[Presentation Details]
Nov. 21 Presentation Day [slides]



We will review your work individually to ensure that you receive due credit for your work. Please note that both your project output and logic will be considered for marking.

Policy and honor code:

You are free to discuss ideas and implementation details with other students. However, copying others’ codes will not help your study but jeopardize it. We will check your work against other submissions and Internet sources. It is easy to know if you did your own work or not. To be clear, we encourage you to discuss with your classmates but you MUST do your work independently and CANNOT simply copy. If plagiarism is identified, one may face serious consequences according to the Faculty and University policy.