COS 484: Natural Language Processing
Spring 2025
[Information]   [Schedule]   [Coursework]   [FAQ]

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What is this course about?

Recent advances have ushered in exciting developments in natural language processing (NLP), resulting in systems that can translate text, answer questions and even hold spoken conversations with us. This course will introduce students to the basics of NLP, covering standard frameworks for dealing with natural language as well as algorithms and techniques to solve various NLP problems, including recent deep learning approaches. Topics covered include language modeling, representation learning, text classification, sequence tagging, machine translation, Transformers, and others.

Information

Course staff:

Time/location:

(All times are in EST.)

Grading

Prerequisites:

Reading:

There is no required textbook for this class, and you should be able to learn everything from the lectures and assignments. However, if you would like to pursue more advanced topics or get another perspective on the same material, here are some books (all of them can be read free online):

Previous offerings:

Schedule

Lecture schedule is tentative and subject to change. All assignments are due at 1:59pm EST before the lectures.

Week Date Topics Readings Assignments
1 Mon (1/27) Introduction to NLP 1. Advances in natural language processing
2. Human Language Understanding & Reasoning 		A0 out
Wed (1/29) n-gram language models, video J & M 3.1-3.5
Fri (1/31) Precept 1
2 Mon (2/3) Text classification Naive Bayes: J & M 4.1-4.6
Logistic regression: J & M 5.1-5.8 		A0 due, A1 out
Wed (2/5) Word embeddings 1, video J & M 5.1-5.6 J & M 6.2-6.4, 6.6
Fri (2/7) Precept 2
3 Mon (2/10) Word embeddings 2, video 1. J & M 6.8, 6.10-6.12
2. Efficient Estimation of Word Representations in Vector Space (original word2vec paper)
3. Distributed representations of words and phrases and their compositionality (negative sampling)
Wed (2/12) Sequence models 1, video 1. J&M 17.1-17.4
2. Michael Collin's notes on HMMs
Fri (2/14) Precept 3
4 Mon (2/17) Sequence models 2, video 1. Michael Collins's notes on MEMMs and CRFs
2. Michael Collins's notes on CRFs 		A1 due, A2 out
Wed (2/19) Neural networks for NLP, video J&M 7.3-7.5
Fri (2/21) Precept 4
5 Mon (2/24) Recurrent neural networks 1
Wed (2/26) Recurrent neural networks 2
Fri (2/28) Precept 5
6 Mon (3/3) Midterm review A2 due
Wed (3/5) Midterm
7 Mon (3/10) Spring break (no class) A3 out
Wed (3/12) Spring break (no class)
8 Mon (3/17) Machine translation
Wed (3/19) Seq2seq models + attention
Fri (3/21) Precept 6 Project proposal due
9 Mon (3/24) Transformers 1
Wed (3/26) Transformers 2
Fri (3/28) Precept 7
10 Mon (3/31) Contextualized embeddings and pre-training A3 due, A4 out
Wed (4/2) Large language models 1
Fri (4/4) Precept 8
11 Mon (4/7) Large language models 2
Wed (4/9) Large language models 3
Fri (4/11) Precept 9
12 Mon (4/14) Systems for LLM training and inference
Wed (4/16) Guest lecture
Fri (4/18) Precept 10
13 Mon (4/21) Guest lecture A4 due
Wed (4/23) Guest lecture
Sun (5/11) Final assessment Final project report due at 3:30pm
Coursework

Assignments

All assignments are due at 1:59pm before the Monday lecture. We allow each student up to 4 free late days that you can use any time during the semester, with at most 3 late days per assignment. No assignment will be accepted more than 3 days after the deadline. We trust you to use these late days responsibly to help mitigate special circumstances. Late hours will round up to a full day (i.e. if you submitted 4 hours late, it will cost you 1 late day). Once you run out of late days, each additional day late will incur a fixed 10% deduction for the given assignment (e.g. 10 points off a 100-point assignment). For extenuating circumstances (e.g, those that involve a Dean), please make sure to email the Dean and the course instructors. For students with a dean’s note, the weight of their missed/penalized assignment will be added to the midterm and your midterm score will be scaled accordingly (for homeworks 0, 1 and 2) (e.g. if you are penalized 2 points overall, your midterm will be worth 27 and your score will be multiplied by 27/25). Missing homework 3 and 4 after the midterm can only be compensated by arranging an oral exam on the pertinent material.
Writeups: Homeworks should be written up clearly and succinctly; you may lose points if your answers are unclear or unnecessarily complicated. Using LaTeX is recommended (here's a template), but not a requirement. If you've never used LaTeX before, refer to this introductory guide on Working with LaTeX to get started. Hand-written assignments must be scanned and uploaded as a pdf.
Programming: For each assignment, we provide a Google Colab file with the programming questions included. You’ll need to make a copy of this file, fill in necessary parts, run results, and upload the code and results as a PDF file. You should also include the ipynb file in your submission. If you've never used Google Colab before, refer to this introductory guide on Working with Google Colab to get started.
Collaboration policy and honor code: You are free to form study groups and discuss homeworks and projects. However, you must write up homeworks and code from scratch independently, and you must acknowledge in your submission all the students you discussed with. The following are considered to be honor code violations (in addition to the Princeton honor code):
  • Looking at the writeup or code of another student.
  • Showing your writeup or code to another student.
  • Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's answer.
  • Uploading your writeup or code (or released solutions) to a public repository (e.g. github, bitbucket, pastebin) so that it can be accessed by other students.
When debugging code together, you are only allowed to look at the input-output behavior of each other's programs (so you should write good test cases!). It is important to remember that even if you didn't copy but just gave another student your solution, you are still violating the honor code, so please be careful. If you feel like you made a mistake (it can happen, especially under time pressure!), please reach out to the instructors; the consequences will be much less severe than if we approach you.
Large language model policy: You may not consult a large language model (LLM) when working on assignment (e.g. you should not feed in assignment questions to an LLM or ask it to write a function and/or code snippet required in an assignment, similar to how you can’t look up assignment solutions on Google). Note that using Github Copilot is considered consulting an LLM. You may use LLMs to help understanding concepts and to study for exams (e.g. like looking up YouTube videos explaining CNNs). However, note that LLMs can generate incorrect text and that they often are equally confident when they are correct vs. incorrect (this is analogous to the fact that not everything presented on YouTube is factually correct). Our course materials are the only standard with which exams and assessments will be evaluated.

Final Project

The final project offers you the chance to apply your newly acquired skills towards an in-depth NLP application. Students are required to complete the final project in teams of 3 students.

There are two options for the final project: (a) reproducing an ACL/NAACL/EMNLP 2021-2023 paper (encouraged); (b) complete a research project (for this option, you need to discuss your proposal and get prior approval from the instructor). All the final projects will be completed in teams of 3 students (Find your teammates early!).
Deliverables: The final project is worth 35% of your course grade. The deliverables include:
Policy and honor code:
  • The final projects are required to be implemented in Python. You can use any deep learning framework such as PyTorch and Tensorflow.
  • You are free to discuss ideas and implementation details with other teams. However, under no circumstances may you look at another team's code, or incorporate their code into your project.
  • Do not share your code publicly (e.g. in a public GitHub repo) until after the class has finished.

Submission

Electronic Submission: Assignments and project proposal/paper are to be submitted as pdf files through Gradescope. If you need to sign up for a Gradescope account, please use your @princeton.edu email address. You can submit as many times as you'd like until the deadline: we will only grade the last submission. Submit early to make sure your submission uploads/runs properly on the Gradescope servers. If anything goes wrong, please ask a question on Ed or contact a TA. Do not email us your submission. Partial work is better than not submitting any work. For more detailed information on submitting your assignment solutions, see this guide on assignment submission logistics.

For assignments with a programming component, we may automatically sanity check your code with some basic test cases, but we will grade your code on additional test cases. Important: just because you pass the basic test cases, you are by no means guaranteed to get full credit on the other, hidden test cases, so you should test the program more thoroughly yourself!

Regrades: If you believe that the course staff made an objective error in grading, then you may submit a regrade request. Remember that even if the grading seems harsh to you, the same rubric was used for everyone for fairness, so this is not sufficient justification for a regrade. It is also helpful to cross-check your answer against the released solutions. If you still choose to submit a regrade request, click the corresponding question on Gradescope, then click the "Request Regrade" button at the bottom. Any requests submitted over email or in person will be ignored. Regrade requests for a particular assignment are due one week after the grades are returned. Note that we may regrade your entire submission, so depending on your submission you may actually lose more points than you gain.

FAQ