All assignments are due at 11:59pm EST on Tuesdays. 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.
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 the Theory portions of assignments. You may use coding assistants like GitHub Copilot/Cursor for completing the programming parts of the homework but we require you to document your use of these assistants in your submission (details will be provided in the assignment). You may also use LLMs to help understand 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.
[Project guidelines are available here]
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/COLM, or NLP papers from ICLR/ICML/NeurIPS from past 5 years (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:
- Proposal (0%): You need to turn in a one-page proposal on March 21st. The proposal should outline what you propose to do and a rough plan for how you will pursue the project. We will then provide feedback and guidance on the direction to maximize the project’s chance
of succeeding. This proposal is not graded.
- Project presentation (10%): At the end of the semester, we will schedule project presentations for all the projects in the class.
- Final paper (25%): You need to complete a final report in the style of a conference submission (we recommend you to use the ACL 2023 template). It should begin with an abstract and introduction, clearly describe the proposed idea or exploration, present technical details, give results, compare to baselines, provide analysis and discussion of the
results, and cite any sources you used.
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.
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.
