Note: In the 2023–24 academic year, CS224N will be taught in both Winter and Spring 2024. We hope to see you in class!

Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks. In this course, students gain a thorough introduction to cutting-edge neural networks for NLP.

Instructors

Course Manager

Amelie Byun

Course Coordinator

John Cho

Logistics

Content

What is this course about?

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics, etc. In the last decade, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework.

“Take it. CS221 taught me algorithms. CS229 taught me math. CS224N taught me how to write machine learning models.” – A CS224N student on Carta

Previous offerings

Below you can find archived websites and student project reports from previous years.

CS224n Websites: Winter 2022 / Winter 2021 / Winter 2020 / Winter 2019 / Winter 2018 / Winter 2017 / Autumn 2015 / Autumn 2014 / Autumn 2013 / Autumn 2012 / Autumn 2011 / Winter 2011 / Spring 2010 / Spring 2009 / Spring 2008 / Spring 2007 / Spring 2006 / Spring 2005 / Spring 2004 / Spring 2003 / Spring 2002 / Spring 2000
CS224n Lecture Videos: Winter 2021 / Winter 2019 / Winter 2017
CS224n Reports: Winter 2021 / Winter 2020 / Winter 2019 / Winter 2018 / Winter 2017 / Autumn 2015 and earlier
CS224d Reports: Spring 2016 / Spring 2015

Prerequisites

Reference Texts

The following texts are useful, but none are required. All of them can be read free online.

If you have no background in neural networks but would like to take the course anyway, you might well find one of these books helpful to give you more background:


Coursework

Assignments (54%)

There are five weekly assignments, which will improve both your theoretical understanding and your practical skills. All assignments contain both written questions and programming parts. In office hours, TAs may look at students’ code for assignments 1, 2 and 3 but not for assignments 4 and 5.

Final Project (43%)

The Final Project offers you the chance to apply your newly acquired skills towards an in-depth application. Students have two options: the Default Final Project (in which students tackle a predefined task, namely implementing a minimalist version of BERT) or a Custom Final Project (in which students choose their own project involving human language and deep learning). Examples of both can be seen on last year's website.

Important information

Practicalities

Participation (3%)

We appreciate everyone being actively involved in the class! There are several ways of earning participation credit, which is capped at 3%:

Late Days

Regrade Requests

If you feel you deserved a better grade on an assignment, you may submit a regrade request on Gradescope within 3 days after the grades are released. Your request should briefly summarize why you feel the original grade was unfair. Your TA will reevaluate your assignment as soon as possible, and then issue a decision. If you are still not happy, you can ask for your assignment to be regraded by an instructor.

Credit/No credit enrollment

If you take the class credit/no credit then you are graded in the same way as those registered for a letter grade. The only difference is that, providing you reach a C- standard in your work, it will simply be graded as CR.

All students welcome

We are committed to doing what we can to work for equity and to create an inclusive learning environment that actively values the diversity of backgrounds, identities, and experiences of everyone in CS224N. We also know that we will sometimes make missteps. If you notice some way that we could do better, we hope that you will let someone in the course staff know about it.

Well-Being and Mental Health

We’re here for you to try to help you get through a couple more quarters of the pandemic. If you are experiencing personal, academic, or relationship problems and would like to talk to someone with training and experience, reach out to the Counseling and Psychological Services (CAPS) on campus. CAPS is the university’s counseling center dedicated to student mental health and wellbeing. Phone assessment appointments can be made at CAPS by calling 650-723-3785, or by accessing the VadenPatient portal through the Vaden website.

Auditing the course

In general we are happy to have auditors if they are a member of the Stanford community (registered student, official visitor, staff, or faculty). If you want to actually master the material of the class, we very strongly recommend that auditors do all the assignments. However, due to high enrollment, we cannot grade the work of any students who are not officially enrolled in the class.

Students with Documented Disabilities

We assume that all of us learn in different ways, and that the organization of the course must accommodate each student differently. We are committed to ensuring the full participation of all enrolled students in this class. If you need an academic accommodation based on a disability, you should initiate the request with the Office of Accessible Education (OAE). The OAE will evaluate the request, recommend accommodations, and prepare a letter for faculty. Students should contact the OAE as soon as possible and at any rate in advance of assignment deadlines, since timely notice is needed to coordinate accommodations. Students should also send your accommodation letter to either the staff mailing list (cs224n-win2223-staff@lists.stanford.edu) or make a private post on Ed, as soon as possible.

Sexual violence

Academic accommodations are available for students who have experienced or are recovering from sexual violence. If you would like to talk to a confidential resource, you can schedule a meeting with the Confidential Support Team or call their 24/7 hotline at: 650-725-9955. Counseling and Psychological Services also offers confidential counseling services. Non-confidential resources include the Title IX Office, for investigation and accommodations, and the SARA Office, for healing programs. Students can also speak directly with the teaching staff to arrange accommodations. Note that university employees – including professors and TAs – are required to report what they know about incidents of sexual or relationship violence, stalking and sexual harassment to the Title IX Office. Students can learn more at https://vaden.stanford.edu/sexual-assault.


Schedule

Updated lecture slides will be posted here shortly before each lecture. Other links contain last year's slides, which are mostly similar.

Lecture notes will be uploaded a few days after most lectures. The notes (which cover approximately the first half of the course content) give supplementary detail beyond the lectures.

Date Description Course Materials Events Deadlines
Tue Jan 10

Week 1
Word Vectors (by John Hewitt)
[slides] [notes]

Gensim word vectors example:
[code] [preview]
Suggested Readings:
  1. Efficient Estimation of Word Representations in Vector Space (original word2vec paper)
  2. Distributed Representations of Words and Phrases and their Compositionality (negative sampling paper)
Assignment 1 out
[code]
[preview]
Thu Jan 12 Word Vectors, Word Window Classification, Language Models
[slides] [notes]
Suggested Readings:
  1. GloVe: Global Vectors for Word Representation (original GloVe paper)
  2. Improving Distributional Similarity with Lessons Learned from Word Embeddings
  3. Evaluation methods for unsupervised word embeddings
Additional Readings:
  1. A Latent Variable Model Approach to PMI-based Word Embeddings
  2. Linear Algebraic Structure of Word Senses, with Applications to Polysemy
  3. On the Dimensionality of Word Embedding
Fri Jan 13 Python Review Session
[slides] [colab]
2:30pm - 3:20pm
Gates B03
Tue Jan 17

Week 2
Backprop and Neural Networks
[slides] [notes]
Suggested Readings:
  1. matrix calculus notes
  2. Review of differential calculus
  3. CS231n notes on network architectures
  4. CS231n notes on backprop
  5. Derivatives, Backpropagation, and Vectorization
  6. Learning Representations by Backpropagating Errors (seminal Rumelhart et al. backpropagation paper)
Additional Readings:
  1. Yes you should understand backprop
  2. Natural Language Processing (Almost) from Scratch
Assignment 2 out
[code]
[handout]
[latex template]
Assignment 1 due
Thu Jan 19 Dependency Parsing
[slides] [notes]
[slides (annotated)]
Suggested Readings:
  1. Incrementality in Deterministic Dependency Parsing
  2. A Fast and Accurate Dependency Parser using Neural Networks
  3. Dependency Parsing
  4. Globally Normalized Transition-Based Neural Networks
  5. Universal Stanford Dependencies: A cross-linguistic typology
  6. Universal Dependencies website
  7. Jurafsky & Martin Chapter 14
Fri Jan 20 PyTorch Tutorial Session
[colab notebook]
3:30pm - 4:20pm
Gates B01
Tue Jan 24

Week 3
Recurrent Neural Networks and Language Models
[slides] [notes (lectures 5 and 6)]
Suggested Readings:
  1. N-gram Language Models (textbook chapter)
  2. The Unreasonable Effectiveness of Recurrent Neural Networks (blog post overview)
  3. Sequence Modeling: Recurrent and Recursive Neural Nets (Sections 10.1 and 10.2)
  4. On Chomsky and the Two Cultures of Statistical Learning
  5. Sequence Modeling: Recurrent and Recursive Neural Nets (Sections 10.3, 10.5, 10.7-10.12)
  6. Learning long-term dependencies with gradient descent is difficult (one of the original vanishing gradient papers)
  7. On the difficulty of training Recurrent Neural Networks (proof of vanishing gradient problem)
  8. Vanishing Gradients Jupyter Notebook (demo for feedforward networks)
  9. Understanding LSTM Networks (blog post overview)
Assignment 3 out
[code]
[handout]
[latex template]
Assignment 2 due
Thu Jan 26 Seq2Seq, MT, Subword Models
[slides] [notes (lectures 5 and 6)]
Suggested Readings:
  1. Statistical Machine Translation slides, CS224n 2015 (lectures 2/3/4)
  2. Statistical Machine Translation (book by Philipp Koehn)
  3. BLEU (original paper)
  4. Sequence to Sequence Learning with Neural Networks (original seq2seq NMT paper)
  5. Sequence Transduction with Recurrent Neural Networks (early seq2seq speech recognition paper)
  6. Neural Machine Translation by Jointly Learning to Align and Translate (original seq2seq+attention paper)
  7. Attention and Augmented Recurrent Neural Networks (blog post overview)
  8. Massive Exploration of Neural Machine Translation Architectures (practical advice for hyperparameter choices)
  9. Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
  10. Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Tue Jan 31

Week 4
Final Projects: Custom and Default; Practical Tips
[slides] [notes]
Suggested Readings:
  1. Practical Methodology (Deep Learning book chapter)
Assignment 4 out
[code]
[handout]
[latex template]
[colab]
Assignment 3 due
Thu Feb 2 Self-Attention and Transformers (by John Hewitt)
[slides] [notes]
Suggested Readings:
  1. Default Project Handout
  2. Attention Is All You Need
  3. The Illustrated Transformer
  4. Transformer (Google AI blog post)
  5. Layer Normalization
  6. Image Transformer
  7. Music Transformer: Generating music with long-term structure
Project Proposal out
[instructions]

Default Final Project out
[handout ]
Tue Feb 7

Week 5
Pretraining (by John Hewitt)
[slides]
Suggested Readings:
  1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  2. Contextual Word Representations: A Contextual Introduction
  3. The Illustrated BERT, ELMo, and co.
  4. Martin & Jurafsky Chapter on Transfer Learning
Thu Feb 9 Natural Language Generation (by Xiang Lisa Li)
[slides]
Suggested Readings:
  1. The Curious Case of Neural Text Degeneration
  2. Get To The Point: Summarization with Pointer-Generator Networks
  3. Hierarchical Neural Story Generation
  4. How NOT To Evaluate Your Dialogue System
Assignment 5 out
[code]
[handout]
[latex template] [colab]
Assignment 4 due
Fri Feb 10 Hugging Face Transformers Tutorial Session 3:30 PM - 4:20 PM
Gates B01
Colab
Tue Feb 14

Week 6
Prompting, Reinforcement Learning from Human Feedback (by Jesse Mu)
[slides]
Suggested Readings:
  1. Language Models are Few-Shot Learners
  2. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  3. Finetuned Language Models Are Zero-Shot Learners
  4. Learning to summarize from human feedback
Project Proposal due
Thu Feb 16 Question Answering
[slides]
Suggested readings:
  1. SQuAD: 100,000+ Questions for Machine Comprehension of Text
  2. Bidirectional Attention Flow for Machine Comprehension
  3. Reading Wikipedia to Answer Open-Domain Questions
  4. Latent Retrieval for Weakly Supervised Open Domain Question Answering
  5. Dense Passage Retrieval for Open-Domain Question Answering
  6. Learning Dense Representations of Phrases at Scale
Project Milestone out
[Instructions]
Sat Feb 18
Assignment 5 due
(11:59 PM)
Tue Feb 21

Week 7
ConvNets, Tree Recursive Neural Networks and Constituency Parsing
[slides]
Suggested readings:
  1. Convolutional Neural Networks for Sentence Classification
  2. Improving neural networks by preventing co-adaptation of feature detectors
  3. A Convolutional Neural Network for Modelling Sentences
  4. Parsing with Compositional Vector Grammars.
  5. Constituency Parsing with a Self-Attentive Encoder
Thu Feb 23 Insights between NLP and Linguistics (by Isabel Papadimitriou)
[slides]
Tue Feb 28

Week 8
Code Generation (by Gabriel Poesia)
[slides]
Suggested readings:
  1. Program Synthesis with Large Language Models
  2. Competition-level code generation with AlphaCode
  3. Evaluating Large Language Models Trained on Code
Wed Mar 1 Training Large Language Models (by John Hewitt) 3:30pm - 4:20pm
Skilling Auditorium
Thu Mar 2 Multimodal Deep Learning (by Douwe Kiela)
[slides]
Fri Mar 3
Project Milestone due
Tue Mar 7

Week 9
Coreference Resolution
[slides]
Suggested readings:
  1. Coreference Resolution Chapter from Jurafsky and Martin
  2. End-to-end Neural Coreference Resolution
Thu Mar 9 Analysis and Interpretability Basics (by John Hewitt)
[slides]
Fri Mar 10 Latex Tutorial (by Rishi Desai) 3:30pm - 4:20pm
Skilling Auditorium
Tue Mar 14

Week 10
Model Interpretability and Editing (by Been Kim)
[slides]
Thu Mar 16 Final Project Emergency Assistance (no lecture) Extra project office hours available during usual lecture time, see Ed.
Sat Mar 18 Project due [instructions]
Monday Mar 20 Poster Session 5pm-9pm [More details]
Location: Tressider Oak Lounge
[Printing guide]